Personal Loan Bank Campaign

Context

AllLife Bank is a US bank that has a growing customer base. The majority of these customers are liability customers (depositors) with varying sizes of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors).

A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio.

You as a Data scientist at AllLife bank have to build a model that will help the marketing department to identify the potential customers who have a higher probability of purchasing the loan.

AI Generated Image

Objectives

To predict whether a liability customer will buy a personal loan or not. Which variables are most significant. Which segment of customers should be targeted more.

Data Dictionary

• ID: Customer ID
• Age: Customer’s age in completed years
• Experience: #years of professional experience
• Income: Annual income of the customer (in thousand dollars)
• ZIP Code: Home Address ZIP code.
• Family: the Family size of the customer
• CCAvg: Average spending on credit cards per month (in thousand dollars)
• Education: Education Level. 1: Undergrad; 2: Graduate;3: Advanced/Professional
• Mortgage: Value of house mortgage if any. (in thousand dollars)
• Personal_Loan: Did this customer accept the personal loan offered in the last campaign?
• Securities_Account: Does the customer have securities account with the bank?
• CD_Account: Does the customer have a certificate of deposit (CD) account with the bank?
• Online: Do customers use internet banking facilities?
• CreditCard: Does the customer use a credit card issued by any other Bank (excluding All life Bank)?

Setup

Basic Setup and Imports

#ignoring warnings
  import warnings
  warnings.filterwarnings("ignore")
  

# Libraries to help with reading and manipulating data
  import numpy as np # linear algebra
  import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

  # libaries to help with data visualization
  import matplotlib.pyplot as plt       # matplotlib.pyplot plots data
  %matplotlib inline
  import seaborn as sns
  pd.set_option('mode.chained_assignment', None)

  #restrict float value to 3 decimal places
  pd.set_option('display.float_format',lambda x: '%.3f' % x)
  %matplotlib inline

  # Removes the limit for the number of displayed columns
  pd.set_option("display.max_columns", None)
  # Sets the limit for the number of displayed rows
  pd.set_option("display.max_rows", 200)

  from sklearn.linear_model import LogisticRegression

  # To get diferent metric scores


  # Library to split data
  from sklearn.model_selection import train_test_split

  from sklearn.metrics import (
      f1_score,
      accuracy_score,
      recall_score,
      precision_score,
      confusion_matrix,
      roc_auc_score,
      ConfusionMatrixDisplay,
      #plot_confusion_matrix,
      precision_recall_curve,
      roc_curve,
  )
  

#allow access to google drive
  from google.colab import drive
  drive.mount('/content/gdrive')
  

Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
  

#paths
  datapath= "/content/gdrive/MyDrive/Projects/ML/Data/"

  #load csv into panda
  ploan_df = pd.read_csv(datapath+ 'Loan_Modelling.csv')
  

# copying data to another variable to avoid any changes to original data
  ploancp_df = ploan_df.copy()
  

Function Definitions

#Function to show %
  def autopct_format(values):
          def my_format(pct):
              total = sum(values)
              val = int(round(pct*total/100.0))
              return '{:.1f}%\n({v:d})'.format(pct, v=val)
          return my_format


  # function to create labeled barplots

  def labeled_barplot(data, feature, perc=False, n=None):
      """
      Barplot with percentage at the top

      data: dataframe
      feature: dataframe column
      perc: whether to display percentages instead of count (default is False)
      n: displays the top n category levels (default is None, i.e., display all levels)
      """

      total = len(data[feature])  # length of the column
      count = data[feature].nunique()
      if n is None:
          plt.figure(figsize=(count + 1, 5))
      else:
          plt.figure(figsize=(n + 1, 5))

      plt.xticks(rotation=90, fontsize=15)
      ax = sns.countplot(
          data=data,
          x=feature,
          palette="Paired",
          order=data[feature].value_counts().index[:n].sort_values(),
      )

      for p in ax.patches:
          if perc == True:
              label = "{:.1f}%".format(
                  100 * p.get_height() / total
              )  # percentage of each class of the category
          else:
              label = p.get_height()  # count of each level of the category

          x = p.get_x() + p.get_width() / 2  # width of the plot
          y = p.get_height()  # height of the plot

          ax.annotate(
              label,
              (x, y),
              ha="center",
              va="center",
              size=12,
              xytext=(0, 5),
              textcoords="offset points",
          )  # annotate the percentage

      plt.show()  # show the plot


  #function to show stacked barplots
  def stacked_barplot(data, predictor, target):
      """
      Print the category counts and plot a stacked bar chart

      data: dataframe
      predictor: independent variable
      target: target variable
      """
      count = data[predictor].nunique()
      sorter = data[target].value_counts().index[-1]
      tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
          by=sorter, ascending=False
      )
      print(tab1)
      print("-" * 120)
      tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
          by=sorter, ascending=False
      )
      tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
      plt.legend(
          loc="lower left", frameon=False,
      )
      plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
      plt.show()

  ### function to plot distributions wrt target

  def distribution_plot_wrt_target(data, predictor, target):

      fig, axs = plt.subplots(2, 2, figsize=(12, 10))

      target_uniq = data[target].unique()

      axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
      sns.histplot(
          data=data[data[target] == target_uniq[0]],
          x=predictor,
          kde=True,
          ax=axs[0, 0],
          color="teal",
          stat="density",
      )

      axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
      sns.histplot(
          data=data[data[target] == target_uniq[1]],
          x=predictor,
          kde=True,
          ax=axs[0, 1],
          color="orange",
          stat="density",
      )

      axs[1, 0].set_title("Boxplot w.r.t target")
      sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

      axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
      sns.boxplot(
          data=data,
          x=target,
          y=predictor,
          ax=axs[1, 1],
          showfliers=False,
          palette="gist_rainbow",
      )

      plt.tight_layout()
      plt.show()


  # defining a function to compute different metrics to check performance of a classification model built using sklearn

  def model_performance_classification_sklearn_with_threshold(model, predictors, target, threshold=0.5):
      """
      Function to compute different metrics, based on the threshold specified, to check classification model performance

      model: classifier
      predictors: independent variables
      target: dependent variable
      threshold: threshold for classifying the observation as class 1
      """

      # predicting using the independent variables
      pred_prob = model.predict_proba(predictors)[:, 1]
      pred_thres = pred_prob > threshold
      pred = np.round(pred_thres)

      acc = accuracy_score(target, pred)  # to compute Accuracy
      recall = recall_score(target, pred)  # to compute Recall
      precision = precision_score(target, pred)  # to compute Precision
      f1 = f1_score(target, pred)  # to compute F1-score

      # creating a dataframe of metrics
      df_perf = pd.DataFrame(
          {
              "Accuracy": acc,
              "Recall": recall,
              "Precision": precision,
              "F1": f1,
          },
          index=[0],
      )

      return df_perf


  # defining a function to plot the confusion_matrix of a classification model built using sklearn
  def confusion_matrix_sklearn_with_threshold(model, predictors, target, threshold=0.5):
      """
      To plot the confusion_matrix, based on the threshold specified, with percentages

      model: classifier
      predictors: independent variables
      target: dependent variable
      threshold: threshold for classifying the observation as class 1
      """
      pred_prob = model.predict_proba(predictors)[:, 1]
      pred_thres = pred_prob > threshold
      y_pred = np.round(pred_thres)

      cm = confusion_matrix(target, y_pred)
      labels = np.asarray(
          [
              ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
              for item in cm.flatten()
          ]
      ).reshape(2, 2)

      plt.figure(figsize=(6, 4))
      sns.heatmap(cm, annot=labels, fmt="")
      plt.ylabel("True label")
      plt.xlabel("Predicted label")
  

Quick Dataset Overview

View Header and Tail

ploancp_df.head()
  

	ID	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard
0	1	25	1	49	91107	4	1.600	1	0	0	1	0	0	0
1	2	45	19	34	90089	3	1.500	1	0	0	1	0	0	0
2	3	39	15	11	94720	1	1.000	1	0	0	0	0	0	0
3	4	35	9	100	94112	1	2.700	2	0	0	0	0	0	0
4	5	35	8	45	91330	4	1.000	2	0	0	0	0	0	1

ploancp_df.tail()
  

	ID	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard
4995	4996	29	3	40	92697	1	1.900	3	0	0	0	0	1	0
4996	4997	30	4	15	92037	4	0.400	1	85	0	0	0	1	0
4997	4998	63	39	24	93023	2	0.300	3	0	0	0	0	0	0
4998	4999	65	40	49	90034	3	0.500	2	0	0	0	0	1	0
4999	5000	28	4	83	92612	3	0.800	1	0	0	0	0	1	1

Visualize the shape and data types of dataset

ploancp_df.shape
  

(5000, 14)

print("The dataset has", ploancp_df.shape[0], "rows and",ploancp_df.shape[1],"columns.")
  number_accounts = ploancp_df.shape[0]
  

The dataset has 5000 rows and 14 columns.
  

ploancp_df.info()
  

<class 'pandas.core.frame.DataFrame'>
  RangeIndex: 5000 entries, 0 to 4999
  Data columns (total 14 columns):
   #   Column              Non-Null Count  Dtype
  ---  ------              --------------  -----
   0   ID                  5000 non-null   int64
   1   Age                 5000 non-null   int64
   2   Experience          5000 non-null   int64
   3   Income              5000 non-null   int64
   4   ZIPCode             5000 non-null   int64
   5   Family              5000 non-null   int64
   6   CCAvg               5000 non-null   float64
   7   Education           5000 non-null   int64
   8   Mortgage            5000 non-null   int64
   9   Personal_Loan       5000 non-null   int64
   10  Securities_Account  5000 non-null   int64
   11  CD_Account          5000 non-null   int64
   12  Online              5000 non-null   int64
   13  CreditCard          5000 non-null   int64
  dtypes: float64(1), int64(13)
  memory usage: 547.0 KB
  

We can see that all the features are numerical, also that there are no NaN values. However notice that some features are better as Categorical/Object type than Numerical.

Summary

ploancp_df.describe().T
  

	count	mean	std	min	25%	50%	75%	max
ID	5000.000	2500.500	1443.520	1.000	1250.750	2500.500	3750.250	5000.000
Age	5000.000	45.338	11.463	23.000	35.000	45.000	55.000	67.000
Experience	5000.000	20.105	11.468	-3.000	10.000	20.000	30.000	43.000
Income	5000.000	73.774	46.034	8.000	39.000	64.000	98.000	224.000
ZIPCode	5000.000	93169.257	1759.455	90005.000	91911.000	93437.000	94608.000	96651.000
Family	5000.000	2.396	1.148	1.000	1.000	2.000	3.000	4.000
CCAvg	5000.000	1.938	1.748	0.000	0.700	1.500	2.500	10.000
Education	5000.000	1.881	0.840	1.000	1.000	2.000	3.000	3.000
Mortgage	5000.000	56.499	101.714	0.000	0.000	0.000	101.000	635.000
Personal_Loan	5000.000	0.096	0.295	0.000	0.000	0.000	0.000	1.000
Securities_Account	5000.000	0.104	0.306	0.000	0.000	0.000	0.000	1.000
CD_Account	5000.000	0.060	0.238	0.000	0.000	0.000	0.000	1.000
Online	5000.000	0.597	0.491	0.000	0.000	1.000	1.000	1.000
CreditCard	5000.000	0.294	0.456	0.000	0.000	0.000	1.000	1.000

• ID is the same as index, we can drop this feature.
• Age Average age is 45, and the range is from 23 to 67. The age range seems acceptable.
• Experience (Years of Experience) Average is 20, and the range is from -3 to 43. Negatives values are an error. We must address this later.
• Income Average is 73K and the range is from 8K to 224K. This range seems acceptable.
• Zip Code, This is actually not numerical, but a categorical feature. This may be useful to know which ZIP codes lead to reduced risk customers.
• Family, the Average number of members is 2.4 and the range is from 1 to 4 members. This range is acceptable.
• CCAvg (Monthly Average spending on Credit Card) Average is $1,938, and the range is $0 to $10,000. This range is acceptable.
• Education, This appears as a numerical feature, but it should be categorical. However, according to the feature description, there are only 3 levels of education: a) Undergraduate, b) Graduate, c) Advanced/Professional

I wonder if the dataset only contains customers that have college degrees, so is "Undergraduate" a customer that has completed college education? "Graduate" means someone that has a masters degree? How about "Advanced/Professional", does this refer to a PhD?

We will analyze this in more detail later.

• Mortage, Personal_Loan, Securities_Account, CD_Account, Online, CreditCard are all boolean fields (or numerical 0 or 1).

Data Preparation

Lets do a quick first data preparation process. We can drop the Customer ID.

#Dropping ID
  ploancp_df.drop('ID',axis=1,inplace=True)
  ploancp_df.info()
  

<class 'pandas.core.frame.DataFrame'>
  RangeIndex: 5000 entries, 0 to 4999
  Data columns (total 13 columns):
   #   Column              Non-Null Count  Dtype
  ---  ------              --------------  -----
   0   Age                 5000 non-null   int64
   1   Experience          5000 non-null   int64
   2   Income              5000 non-null   int64
   3   ZIPCode             5000 non-null   int64
   4   Family              5000 non-null   int64
   5   CCAvg               5000 non-null   float64
   6   Education           5000 non-null   int64
   7   Mortgage            5000 non-null   int64
   8   Personal_Loan       5000 non-null   int64
   9   Securities_Account  5000 non-null   int64
   10  CD_Account          5000 non-null   int64
   11  Online              5000 non-null   int64
   12  CreditCard          5000 non-null   int64
  dtypes: float64(1), int64(12)
  memory usage: 507.9 KB
  

Univariate Analysis

Null Values

ploancp_df.isnull().values.any()
  

False

ploancp_df.isnull().sum()
  

Age                   0
  Experience            0
  Income                0
  ZIPCode               0
  Family                0
  CCAvg                 0
  Education             0
  Mortgage              0
  Personal_Loan         0
  Securities_Account    0
  CD_Account            0
  Online                0
  CreditCard            0
  dtype: int64

There are no Null values in the dataset as we had seen earlier.

Change Feature Types

Change features that should be categorical objects: ZIPCode, Education, Personal_Loan Securities_Account, CD_Account, Online, CreditCard

cols_num2cat = ['ZIPCode','Education','Personal_Loan','Securities_Account','CD_Account','Online','CreditCard']
  ploancp_df[cols_num2cat] = ploancp_df[cols_num2cat].astype('object')
  

Review Categorical and Object type features

numeric_cols = ploancp_df.select_dtypes(include=[np.number]).columns
  cat_cols =ploancp_df.describe(include=["object"]).columns

  print("Categorical Columns:     ", cat_cols)
  print("Numeric Columns:    ", numeric_cols)
  

Categorical Columns:      Index(['ZIPCode', 'Education', 'Personal_Loan', 'Securities_Account',
         'CD_Account', 'Online', 'CreditCard'],
        dtype='object')
  Numeric Columns:     Index(['Age', 'Experience', 'Income', 'Family', 'CCAvg', 'Mortgage'], dtype='object')
  

Checking different levels in categorical data

# Checking value counts of categorical variables
  for i in cat_cols:
      print("*" * 50)
      print("Unique values in", i, "are :")
      print(ploancp_df[i].value_counts())
      print("-" * 50)
      print("Unique values Percentages", i, "are :")
      print(ploancp_df[i].value_counts(1)*100)
  

**************************************************
  Unique values in ZIPCode are :
  94720    169
  94305    127
  95616    116
  90095     71
  93106     57
          ...
  96145      1
  94087      1
  91024      1
  93077      1
  94598      1
  Name: ZIPCode, Length: 467, dtype: int64
  --------------------------------------------------
  Unique values Percentages ZIPCode are :
  94720   3.380
  94305   2.540
  95616   2.320
  90095   1.420
  93106   1.140
           ...
  96145   0.020
  94087   0.020
  91024   0.020
  93077   0.020
  94598   0.020
  Name: ZIPCode, Length: 467, dtype: float64
  **************************************************
  Unique values in Education are :
  1    2096
  3    1501
  2    1403
  Name: Education, dtype: int64
  --------------------------------------------------
  Unique values Percentages Education are :
  1   41.920
  3   30.020
  2   28.060
  Name: Education, dtype: float64
  **************************************************
  Unique values in Personal_Loan are :
  0    4520
  1     480
  Name: Personal_Loan, dtype: int64
  --------------------------------------------------
  Unique values Percentages Personal_Loan are :
  0   90.400
  1    9.600
  Name: Personal_Loan, dtype: float64
  **************************************************
  Unique values in Securities_Account are :
  0    4478
  1     522
  Name: Securities_Account, dtype: int64
  --------------------------------------------------
  Unique values Percentages Securities_Account are :
  0   89.560
  1   10.440
  Name: Securities_Account, dtype: float64
  **************************************************
  Unique values in CD_Account are :
  0    4698
  1     302
  Name: CD_Account, dtype: int64
  --------------------------------------------------
  Unique values Percentages CD_Account are :
  0   93.960
  1    6.040
  Name: CD_Account, dtype: float64
  **************************************************
  Unique values in Online are :
  1    2984
  0    2016
  Name: Online, dtype: int64
  --------------------------------------------------
  Unique values Percentages Online are :
  1   59.680
  0   40.320
  Name: Online, dtype: float64
  **************************************************
  Unique values in CreditCard are :
  0    3530
  1    1470
  Name: CreditCard, dtype: int64
  --------------------------------------------------
  Unique values Percentages CreditCard are :
  0   70.600
  1   29.400
  Name: CreditCard, dtype: float64
  

• ZIPCode: Notice that 94720, 94305, 95616, 90095, 93106 represent 10% of all ZIP Codes in the dataset.

• Education: 41.920% have a undergraduate degree, 30.020% a graduate degree, and 28.060% an advanced/professional degree. Are there any customers in the dataset that have no degrees? If the labels are right, all customers have at least a college degree.

• Personal Loan: 90.4% of customers in the dataset do NOT have a personal loan and only 9.6% have a loan.

• Securities Account: 10.44% of customers have a securities account.

• CD Account: 6.040% of customers have a CD account.

• Online Use: 59.680% of customers access their bank account online.

• Credit Card: 29.400% of customers have a credit card with the bank.

Single Feature Distributions Plots

# creating histograms for numeric_cols
  ploancp_df[numeric_cols].hist(figsize=(14, 14))
  plt.show()
  

Notice that roughly Age and Experience are somewhat symmetrical. Income, Mortgage, CCAvg are skewed right. These all make sense.

#create barplots for categorical features

  fig, axes = plt.subplots(2, 3, figsize=(15, 5), sharey=True)
  fig.tight_layout(h_pad=2)
  plt.subplots_adjust(top=0.89)
  fig.suptitle('Distribution - Categorical Features')
  sns.countplot(ax=axes[1, 1], data=ploancp_df, x='Personal_Loan')
  sns.countplot(ax=axes[0, 2], data=ploancp_df, x='Securities_Account')
  sns.countplot(ax=axes[0, 1], data=ploancp_df, x='CD_Account')
  sns.countplot(ax=axes[0, 0], data=ploancp_df, x='Online')
  sns.countplot(ax=axes[1, 0], data=ploancp_df, x='CreditCard')

  plt.show()
  

We noticed that most customers access their account Online, a small percentage of customers have CD Account, a Securities Account or a Personal Loan, and some have a Credit card.

Visualizing these as pie charts may be more useful.

#visualization Single Features as pie plots with percentage of total

  fig, axes = plt.subplots(1, 7, figsize=(18, 18), sharey=True)

  data_fy=ploancp_df['Family'].value_counts()
  data_ed=ploancp_df['Education'].value_counts()
  data_pl=ploancp_df['Personal_Loan'].value_counts()
  data_sa =ploancp_df['Securities_Account'].value_counts()
  data_cd = ploancp_df['CD_Account'].value_counts()
  data_online =ploancp_df['Online'].value_counts()
  data_cc = ploancp_df['CreditCard'].value_counts()

  plt.title("Distribution - Single Feature")

  ax1 = plt.subplot2grid((1,7), (0,0))
  plt.pie(data_fy,labels=data_fy.index, autopct=autopct_format(data_fy),colors=("g","r"))
  plt.title('Family')

  ax1 = plt.subplot2grid((1,7), (0,1))
  plt.pie(data_ed,labels=data_ed.index, autopct=autopct_format(data_ed),colors=("g","r"))
  plt.title('Education')

  ax1 = plt.subplot2grid((1,7),(0,2))
  plt.pie(data_pl,labels=data_pl.index, autopct=autopct_format(data_pl),colors=("g","r"))
  plt.title('Personal Loan')

  ax1 = plt.subplot2grid((1,7), (0, 3))
  plt.pie(data_sa,labels=data_sa.index, autopct=autopct_format(data_sa),colors=("g","r"))
  plt.title('Securities Account')

  ax1 = plt.subplot2grid((1,7), (0, 4))
  plt.pie(data_cd,labels=data_cd.index, autopct=autopct_format(data_cd),colors=("g","r"))
  plt.title('CD Account')

  ax1 = plt.subplot2grid((1,7), (0, 5))
  plt.pie(data_online,labels=data_online.index, autopct=autopct_format(data_online),colors=("g","r"))
  plt.title('Online')

  ax1 = plt.subplot2grid((1,7), (0, 6))
  plt.pie(data_cc,labels=data_cc.index, autopct=autopct_format(data_cc),colors=("g","r"))
  plt.title('Credit Card')

  plt.show()
  

The same features visualized as pie charts with amounts and percentages of the total. Notice that 30% of customers have a credit card with the bank, 40% access their accounts online, 10% have a securities account, 9.6% have a Personal Loan, and 6% have a CD account.

Analysis Feature: Age

Lets take a look at Age in more detail.

plt.figure(figsize=(6,4), dpi= 60)

  plt.title("Age Distribution", fontsize=16)
  sns.boxplot(data=ploancp_df,x='Age')
  plt.show()
  

The age distribution doesn't have any outliers and the median is nicely distributed between Q1 and Q3.

#same but as barplots
  labeled_barplot(ploancp_df, 'Age', perc=True, n=None)
  

Analysis Feature: Experience

We has observed earlier that the min Experience was a negative number. Lets take a look into that.

#How many Experience values are negative
  num_exp_negative= ploancp_df.loc[ploancp_df['Experience']<0].value_counts().sum()
  num_total_cust = ploan_df.shape[0]
  print("The number of customers w/ apparent Negative experience is",num_exp_negative,"which represents",(num_exp_negative/num_total_cust)*100,"% of the total customers.")
  

The number of customers w/ apparent Negative experience is 52 which represents 1.04 % of the total customers.
  

#see the negative experience accounts
  ploancp_df.loc[ploancp_df['Experience']<0]
  

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard
89	25	-1	113	94303	4	2.300	3	0	0	0	0	0	1
226	24	-1	39	94085	2	1.700	2	0	0	0	0	0	0
315	24	-2	51	90630	3	0.300	3	0	0	0	0	1	0
451	28	-2	48	94132	2	1.750	3	89	0	0	0	1	0
524	24	-1	75	93014	4	0.200	1	0	0	0	0	1	0
536	25	-1	43	92173	3	2.400	2	176	0	0	0	1	0
540	25	-1	109	94010	4	2.300	3	314	0	0	0	1	0
576	25	-1	48	92870	3	0.300	3	0	0	0	0	0	1
583	24	-1	38	95045	2	1.700	2	0	0	0	0	1	0
597	24	-2	125	92835	2	7.200	1	0	0	1	0	0	1
649	25	-1	82	92677	4	2.100	3	0	0	0	0	1	0
670	23	-1	61	92374	4	2.600	1	239	0	0	0	1	0
686	24	-1	38	92612	4	0.600	2	0	0	0	0	1	0
793	24	-2	150	94720	2	2.000	1	0	0	0	0	1	0
889	24	-2	82	91103	2	1.600	3	0	0	0	0	1	1
909	23	-1	149	91709	1	6.330	1	305	0	0	0	0	1
1173	24	-1	35	94305	2	1.700	2	0	0	0	0	0	0
1428	25	-1	21	94583	4	0.400	1	90	0	0	0	1	0
1522	25	-1	101	94720	4	2.300	3	256	0	0	0	0	1
1905	25	-1	112	92507	2	2.000	1	241	0	0	0	1	0
2102	25	-1	81	92647	2	1.600	3	0	0	0	0	1	1
2430	23	-1	73	92120	4	2.600	1	0	0	0	0	1	0
2466	24	-2	80	94105	2	1.600	3	0	0	0	0	1	0
2545	25	-1	39	94720	3	2.400	2	0	0	0	0	1	0
2618	23	-3	55	92704	3	2.400	2	145	0	0	0	1	0
2717	23	-2	45	95422	4	0.600	2	0	0	0	0	1	1
2848	24	-1	78	94720	2	1.800	2	0	0	0	0	0	0
2876	24	-2	80	91107	2	1.600	3	238	0	0	0	0	0
2962	23	-2	81	91711	2	1.800	2	0	0	0	0	0	0
2980	25	-1	53	94305	3	2.400	2	0	0	0	0	0	0
3076	29	-1	62	92672	2	1.750	3	0	0	0	0	0	1
3130	23	-2	82	92152	2	1.800	2	0	0	1	0	0	1
3157	23	-1	13	94720	4	1.000	1	84	0	0	0	1	0
3279	26	-1	44	94901	1	2.000	2	0	0	0	0	0	0
3284	25	-1	101	95819	4	2.100	3	0	0	0	0	0	1
3292	25	-1	13	95616	4	0.400	1	0	0	1	0	0	0
3394	25	-1	113	90089	4	2.100	3	0	0	0	0	1	0
3425	23	-1	12	91605	4	1.000	1	90	0	0	0	1	0
3626	24	-3	28	90089	4	1.000	3	0	0	0	0	0	0
3796	24	-2	50	94920	3	2.400	2	0	0	1	0	0	0
3824	23	-1	12	95064	4	1.000	1	0	0	1	0	0	1
3887	24	-2	118	92634	2	7.200	1	0	0	1	0	1	0
3946	25	-1	40	93117	3	2.400	2	0	0	0	0	1	0
4015	25	-1	139	93106	2	2.000	1	0	0	0	0	0	1
4088	29	-1	71	94801	2	1.750	3	0	0	0	0	0	0
4116	24	-2	135	90065	2	7.200	1	0	0	0	0	1	0
4285	23	-3	149	93555	2	7.200	1	0	0	0	0	1	0
4411	23	-2	75	90291	2	1.800	2	0	0	0	0	1	1
4481	25	-2	35	95045	4	1.000	3	0	0	0	0	1	0
4514	24	-3	41	91768	4	1.000	3	0	0	0	0	1	0
4582	25	-1	69	92691	3	0.300	3	0	0	0	0	1	0
4957	29	-1	50	95842	2	1.750	3	0	0	0	0	0	1

#common Experience values and counts
  ploancp_df['Experience'].value_counts()
  

 32    154
   20    148
   9     147
   5     146
   23    144
   35    143
   25    142
   28    138
   18    137
   19    135
   26    134
   24    131
   3     129
   16    127
   14    127
   30    126
   17    125
   34    125
   27    125
   22    124
   29    124
   7     121
   6     119
   15    119
   8     119
   10    118
   13    117
   33    117
   11    116
   37    116
   36    114
   21    113
   4     113
   31    104
   12    102
   38     88
   2      85
   39     85
   1      74
   0      66
   40     57
   41     43
  -1      33
  -2      15
   42      8
  -3       4
   43      3
  Name: Experience, dtype: int64

plt.figure(figsize=(6,4), dpi= 60)
  plt.title("Positive Experience Distribution", fontsize=16)
  sns.boxplot(data=ploancp_df.loc[ploancp_df['Experience']>=0],x='Experience')
  plt.show()
  

Positive Experience is nicely distributed with a median (Q2) around 20 years, Q1 around 11, and Q3 around 30.

plt.figure(figsize=(6,4), dpi= 60)

  plt.title("Negative Experience Distribution", fontsize=16)
  sns.boxplot(data=ploancp_df.loc[ploancp_df['Experience']<0],x='Experience')
  plt.show()
  

#experience = -3
  ploancp_x3_df = ploancp_df.loc[ploancp_df['Experience']==-3]
  ploancp_x3_df
  

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard
2618	23	-3	55	92704	3	2.400	2	145	0	0	0	1	0
3626	24	-3	28	90089	4	1.000	3	0	0	0	0	0	0
4285	23	-3	149	93555	2	7.200	1	0	0	0	0	1	0
4514	24	-3	41	91768	4	1.000	3	0	0	0	0	1	0

Notice that there are only 4 entries with a negative experience -3. Lets contrast them to Age and Education because these 2 other features are also related with time.

#experience = -2
  ploancp_x2_df = ploancp_df.loc[ploancp_df['Experience']==-2]
  ploancp_x2_df
  

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard
315	24	-2	51	90630	3	0.300	3	0	0	0	0	1	0
451	28	-2	48	94132	2	1.750	3	89	0	0	0	1	0
597	24	-2	125	92835	2	7.200	1	0	0	1	0	0	1
793	24	-2	150	94720	2	2.000	1	0	0	0	0	1	0
889	24	-2	82	91103	2	1.600	3	0	0	0	0	1	1
2466	24	-2	80	94105	2	1.600	3	0	0	0	0	1	0
2717	23	-2	45	95422	4	0.600	2	0	0	0	0	1	1
2876	24	-2	80	91107	2	1.600	3	238	0	0	0	0	0
2962	23	-2	81	91711	2	1.800	2	0	0	0	0	0	0
3130	23	-2	82	92152	2	1.800	2	0	0	1	0	0	1
3796	24	-2	50	94920	3	2.400	2	0	0	1	0	0	0
3887	24	-2	118	92634	2	7.200	1	0	0	1	0	1	0
4116	24	-2	135	90065	2	7.200	1	0	0	0	0	1	0
4411	23	-2	75	90291	2	1.800	2	0	0	0	0	1	1
4481	25	-2	35	95045	4	1.000	3	0	0	0	0	1	0

Notice that the Age range for experience=-2 is between 23 to 28.

#experience = -1
  ploancp_x1_df = ploancp_df.loc[ploancp_df['Experience']==-1]
  ploancp_x1_df
  

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard
89	25	-1	113	94303	4	2.300	3	0	0	0	0	0	1
226	24	-1	39	94085	2	1.700	2	0	0	0	0	0	0
524	24	-1	75	93014	4	0.200	1	0	0	0	0	1	0
536	25	-1	43	92173	3	2.400	2	176	0	0	0	1	0
540	25	-1	109	94010	4	2.300	3	314	0	0	0	1	0
576	25	-1	48	92870	3	0.300	3	0	0	0	0	0	1
583	24	-1	38	95045	2	1.700	2	0	0	0	0	1	0
649	25	-1	82	92677	4	2.100	3	0	0	0	0	1	0
670	23	-1	61	92374	4	2.600	1	239	0	0	0	1	0
686	24	-1	38	92612	4	0.600	2	0	0	0	0	1	0
909	23	-1	149	91709	1	6.330	1	305	0	0	0	0	1
1173	24	-1	35	94305	2	1.700	2	0	0	0	0	0	0
1428	25	-1	21	94583	4	0.400	1	90	0	0	0	1	0
1522	25	-1	101	94720	4	2.300	3	256	0	0	0	0	1
1905	25	-1	112	92507	2	2.000	1	241	0	0	0	1	0
2102	25	-1	81	92647	2	1.600	3	0	0	0	0	1	1
2430	23	-1	73	92120	4	2.600	1	0	0	0	0	1	0
2545	25	-1	39	94720	3	2.400	2	0	0	0	0	1	0
2848	24	-1	78	94720	2	1.800	2	0	0	0	0	0	0
2980	25	-1	53	94305	3	2.400	2	0	0	0	0	0	0
3076	29	-1	62	92672	2	1.750	3	0	0	0	0	0	1
3157	23	-1	13	94720	4	1.000	1	84	0	0	0	1	0
3279	26	-1	44	94901	1	2.000	2	0	0	0	0	0	0
3284	25	-1	101	95819	4	2.100	3	0	0	0	0	0	1
3292	25	-1	13	95616	4	0.400	1	0	0	1	0	0	0
3394	25	-1	113	90089	4	2.100	3	0	0	0	0	1	0
3425	23	-1	12	91605	4	1.000	1	90	0	0	0	1	0
3824	23	-1	12	95064	4	1.000	1	0	0	1	0	0	1
3946	25	-1	40	93117	3	2.400	2	0	0	0	0	1	0
4015	25	-1	139	93106	2	2.000	1	0	0	0	0	0	1
4088	29	-1	71	94801	2	1.750	3	0	0	0	0	0	0
4582	25	-1	69	92691	3	0.300	3	0	0	0	0	1	0
4957	29	-1	50	95842	2	1.750	3	0	0	0	0	0	1

Now for Experience=-1, the age range is between 23 and 29.

How about 0 Experience? Is that value registered?

#experience = 0
  ploancp_x0_df = ploancp_df.loc[ploancp_df['Experience']==0]
  x0_median=ploancp_x0_df['Age'].median()
  x0_min=ploancp_x0_df['Age'].min()
  x0_max=ploancp_x0_df['Age'].max()

  ploancp_x0_df
  

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard
105	24	0	35	94704	3	0.100	2	0	0	1	0	1	0
151	26	0	132	92834	3	6.500	3	0	1	0	0	0	1
155	24	0	60	94596	4	1.600	1	0	0	0	0	1	0
156	26	0	15	92131	4	0.400	1	0	0	0	0	0	1
160	29	0	134	95819	4	6.500	3	0	1	0	0	0	0
182	24	0	135	95133	1	1.500	1	0	0	0	0	1	0
256	26	0	99	92697	4	2.300	3	0	0	0	0	0	1
347	25	0	43	94305	2	1.600	3	0	0	1	1	1	1
363	25	0	30	92691	2	1.700	2	0	0	0	0	0	0
379	25	0	28	92093	2	1.700	2	0	0	0	0	0	0
418	27	0	33	90089	4	1.000	3	0	0	0	0	0	0
466	25	0	13	91342	2	0.900	3	0	0	0	0	1	0
495	25	0	44	94545	4	0.600	2	0	0	0	0	1	1
873	24	0	88	90740	3	0.800	1	134	0	0	0	0	0
1057	30	0	63	95503	2	1.750	3	0	0	0	0	1	0
1181	25	0	65	90095	4	0.200	1	0	0	1	0	0	0
1337	26	0	179	92028	4	2.100	2	0	1	0	0	0	0
1732	25	0	88	94566	2	1.800	2	319	0	0	0	1	1
1765	26	0	149	95051	2	7.200	1	154	0	0	0	0	0
1847	25	0	52	95126	3	2.600	3	159	0	0	0	0	0
2009	25	0	99	92735	1	1.900	1	323	0	0	0	0	0
2157	25	0	71	93727	4	0.200	1	78	0	1	0	0	0
2165	27	0	38	95929	4	1.000	3	154	0	0	0	1	0
2241	26	0	14	94301	4	0.400	1	94	0	0	0	1	0
2259	24	0	82	90401	3	0.800	1	0	0	0	0	1	0
2417	25	0	53	90095	2	1.600	3	0	0	0	0	1	1
2648	26	0	155	93105	2	7.200	1	0	0	0	0	0	0
2652	24	0	44	90089	4	1.600	1	180	0	0	0	1	0
2756	27	0	40	91301	4	1.000	3	0	0	0	0	1	0
3075	26	0	85	95616	2	1.600	3	0	0	0	0	0	0
3084	26	0	129	90028	3	0.700	2	0	1	0	0	0	0
3135	25	0	91	95039	2	1.800	2	321	0	0	0	0	0
3147	26	0	30	94024	4	1.300	3	0	0	0	0	1	1
3378	25	0	44	94536	4	0.600	2	0	0	0	0	0	1
3538	26	0	23	93561	1	0.100	2	0	0	0	0	0	0
3747	26	0	83	91360	3	3.900	2	0	1	0	0	1	0
3765	26	0	54	94706	3	0.300	3	0	0	0	0	1	0
3818	26	0	102	94305	4	2.300	3	0	0	0	0	0	0
3870	25	0	25	94596	2	0.900	3	0	0	0	0	0	0
3889	26	0	19	93014	1	0.100	2	121	0	0	0	1	0
3908	24	0	44	90638	3	0.100	2	0	0	0	0	0	0
3982	24	0	119	94566	1	1.500	1	0	0	0	0	1	0
4017	26	0	42	92009	4	1.300	3	153	0	0	0	1	0
4046	25	0	72	94303	3	2.600	3	0	0	0	0	1	0
4077	26	0	71	92093	4	1.800	2	0	0	1	0	1	0
4080	27	0	40	90068	1	2.000	2	110	0	0	0	0	1
4109	27	0	30	93107	4	1.000	3	0	0	0	0	1	1
4282	26	0	195	92093	3	6.330	3	0	1	1	1	1	0
4321	27	0	34	92717	1	2.000	2	112	0	0	0	0	1
4393	24	0	59	95521	4	1.600	1	0	0	0	0	0	0
4425	26	0	164	95973	2	4.000	3	301	1	0	0	1	0
4529	27	0	40	92103	4	1.000	3	0	0	0	0	0	0
4551	27	0	28	91330	4	1.500	2	0	0	0	0	1	0
4566	24	0	131	92831	1	5.400	1	0	0	0	0	1	0
4568	26	0	44	94305	4	1.300	3	0	0	1	0	0	0
4584	26	0	49	90089	3	2.400	2	0	0	0	0	0	0
4677	25	0	38	93407	2	1.600	3	0	0	0	0	0	0
4679	26	0	161	94551	2	7.200	1	0	0	0	0	0	0
4712	25	0	14	94309	2	0.900	3	0	0	0	0	0	1
4782	26	0	150	91311	2	7.200	1	0	0	0	0	0	1
4796	26	0	42	95032	4	1.300	3	0	0	1	0	0	0
4874	26	0	75	94061	3	0.300	3	0	0	0	0	0	0
4901	26	0	54	96094	3	1.100	2	0	0	0	0	1	0
4934	26	0	85	93950	2	1.600	3	0	0	0	0	1	1
4943	26	0	12	96003	1	0.100	2	0	0	0	0	1	0
4989	24	0	38	93555	1	1.000	3	0	0	0	0	1	0

print("For Experience 0, The minimum age is",x0_min, ",the mean age is",x0_median,",the max age is",x0_max)
  

For Experience 0, The minimum age is 24 ,the mean age is 26.0 ,the max age is 30
  

We can see that there are many customers with Experience 0.

#also for our plotting lets have a dataframe that includes all negative Experience.
  ploancp_negEx_df = ploancp_df.loc[ploancp_df['Experience']<0]
  

We have to take care of the negative values.

Look at Experience -3, -2, -1. As we know, negative experience is not a valid value. How can we treat these negative values?

• Change them to the mode or median?
• Replace them with a 0?
• Change them to positive?
• Drop those rows?

We will do this during our bivariate analysis because we need to analyze Experience with other Time dependant features like Age and Education.

Analysis Feature: Income

Lets take a look at income in more detail.

plt.figure(figsize=(6,4), dpi= 60)
  plt.title("Income Distribution", fontsize=16)
  sns.boxplot(x='Income', data=ploancp_df, notch=False)
  plt.show()
  

The median is 73,774 (obtaine from Description) and shown in the boxplot above. Notice that some customers make over 180K but these outliers are valid data.

plt.figure(figsize=(6,3), dpi= 80)
  sns.kdeplot(ploancp_df['Income'], shade=True, color="g", alpha=.7)
  plt.title("Income Distribution")
  plt.show()
  

Lets bin the income to make a bar plot. From Description we know what the min, max, median values are. But lets get them again.

income_min = ploancp_df['Income'].min()
  income_max = ploancp_df['Income'].max()
  income_mean = ploancp_df['Income'].median()
  print("The Income minimum is", income_min, "K, the Income mean is", income_mean,"K, and the Income max is",income_max,"K")
  

The Income minimum is 8 K, the Income mean is 64.0 K, and the Income max is 224 K
  

bins = [0, 25, 50, 75, 100, 125, 150, 175, 200, 225]
  labels = ['[0..25)','[25..50)','[50..75)', '[75..100)','[100..125)','[125..150)','[150..175)','[175..200)','[200..225]']
  ploancp_df['Income_bin'] = pd.cut(ploancp_df['Income'], bins=bins, labels=labels)
  print (ploancp_df)
  

      Age  Experience  Income ZIPCode  Family  CCAvg Education  Mortgage  \
  0      25           1      49   91107       4  1.600         1         0
  1      45          19      34   90089       3  1.500         1         0
  2      39          15      11   94720       1  1.000         1         0
  3      35           9     100   94112       1  2.700         2         0
  4      35           8      45   91330       4  1.000         2         0
  ...   ...         ...     ...     ...     ...    ...       ...       ...
  4995   29           3      40   92697       1  1.900         3         0
  4996   30           4      15   92037       4  0.400         1        85
  4997   63          39      24   93023       2  0.300         3         0
  4998   65          40      49   90034       3  0.500         2         0
  4999   28           4      83   92612       3  0.800         1         0

       Personal_Loan Securities_Account CD_Account Online CreditCard Income_bin
  0                0                  1          0      0          0   [25..50)
  1                0                  1          0      0          0   [25..50)
  2                0                  0          0      0          0    [0..25)
  3                0                  0          0      0          0  [75..100)
  4                0                  0          0      0          1   [25..50)
  ...            ...                ...        ...    ...        ...        ...
  4995             0                  0          0      1          0   [25..50)
  4996             0                  0          0      1          0    [0..25)
  4997             0                  0          0      0          0    [0..25)
  4998             0                  0          0      1          0   [25..50)
  4999             0                  0          0      1          1  [75..100)

  [5000 rows x 14 columns]
  

labeled_barplot(ploancp_df, 'Income_bin', perc=True, n=None)
  

Notice that 25% of customers are in the [25..50) bin, and 20% are in the [50..75) bin.

Analysis Feature: ZIPCode

Lets analyze ZIP Codes in more detail.

#Show unique ZIPCodes.
  ploancp_df['ZIPCode'].unique()
  

array([91107, 90089, 94720, 94112, 91330, 92121, 91711, 93943, 93023,
         94710, 90277, 93106, 94920, 91741, 95054, 95010, 94305, 91604,
         94015, 90095, 91320, 95521, 95064, 90064, 94539, 94104, 94117,
         94801, 94035, 92647, 95814, 94114, 94115, 92672, 94122, 90019,
         95616, 94065, 95014, 91380, 95747, 92373, 92093, 94005, 90245,
         95819, 94022, 90404, 93407, 94523, 90024, 91360, 95670, 95123,
         90045, 91335, 93907, 92007, 94606, 94611, 94901, 92220, 93305,
         95134, 94612, 92507, 91730, 94501, 94303, 94105, 94550, 92612,
         95617, 92374, 94080, 94608, 93555, 93311, 94704, 92717, 92037,
         95136, 94542, 94143, 91775, 92703, 92354, 92024, 92831, 92833,
         94304, 90057, 92130, 91301, 92096, 92646, 92182, 92131, 93720,
         90840, 95035, 93010, 94928, 95831, 91770, 90007, 94102, 91423,
         93955, 94107, 92834, 93117, 94551, 94596, 94025, 94545, 95053,
         90036, 91125, 95120, 94706, 95827, 90503, 90250, 95817, 95503,
         93111, 94132, 95818, 91942, 90401, 93524, 95133, 92173, 94043,
         92521, 92122, 93118, 92697, 94577, 91345, 94123, 92152, 91355,
         94609, 94306, 96150, 94110, 94707, 91326, 90291, 92807, 95051,
         94085, 92677, 92614, 92626, 94583, 92103, 92691, 92407, 90504,
         94002, 95039, 94063, 94923, 95023, 90058, 92126, 94118, 90029,
         92806, 94806, 92110, 94536, 90623, 92069, 92843, 92120, 95605,
         90740, 91207, 95929, 93437, 90630, 90034, 90266, 95630, 93657,
         92038, 91304, 92606, 92192, 90745, 95060, 94301, 92692, 92101,
         94610, 90254, 94590, 92028, 92054, 92029, 93105, 91941, 92346,
         94402, 94618, 94904, 93077, 95482, 91709, 91311, 94509, 92866,
         91745, 94111, 94309, 90073, 92333, 90505, 94998, 94086, 94709,
         95825, 90509, 93108, 94588, 91706, 92109, 92068, 95841, 92123,
         91342, 90232, 92634, 91006, 91768, 90028, 92008, 95112, 92154,
         92115, 92177, 90640, 94607, 92780, 90009, 92518, 91007, 93014,
         94024, 90027, 95207, 90717, 94534, 94010, 91614, 94234, 90210,
         95020, 92870, 92124, 90049, 94521, 95678, 95045, 92653, 92821,
         90025, 92835, 91910, 94701, 91129, 90071, 96651, 94960, 91902,
         90033, 95621, 90037, 90005, 93940, 91109, 93009, 93561, 95126,
         94109, 93107, 94591, 92251, 92648, 92709, 91754, 92009, 96064,
         91103, 91030, 90066, 95403, 91016, 95348, 91950, 95822, 94538,
         92056, 93063, 91040, 92661, 94061, 95758, 96091, 94066, 94939,
         95138, 95762, 92064, 94708, 92106, 92116, 91302, 90048, 90405,
         92325, 91116, 92868, 90638, 90747, 93611, 95833, 91605, 92675,
         90650, 95820, 90018, 93711, 95973, 92886, 95812, 91203, 91105,
         95008, 90016, 90035, 92129, 90720, 94949, 90041, 95003, 95192,
         91101, 94126, 90230, 93101, 91365, 91367, 91763, 92660, 92104,
         91361, 90011, 90032, 95354, 94546, 92673, 95741, 95351, 92399,
         90274, 94087, 90044, 94131, 94124, 95032, 90212, 93109, 94019,
         95828, 90086, 94555, 93033, 93022, 91343, 91911, 94803, 94553,
         95211, 90304, 92084, 90601, 92704, 92350, 94705, 93401, 90502,
         94571, 95070, 92735, 95037, 95135, 94028, 96003, 91024, 90065,
         95405, 95370, 93727, 92867, 95821, 94566, 95125, 94526, 94604,
         96008, 93065, 96001, 95006, 90639, 92630, 95307, 91801, 94302,
         91710, 93950, 90059, 94108, 94558, 93933, 92161, 94507, 94575,
         95449, 93403, 93460, 95005, 93302, 94040, 91401, 95816, 92624,
         95131, 94965, 91784, 91765, 90280, 95422, 95518, 95193, 92694,
         90275, 90272, 91791, 92705, 91773, 93003, 90755, 96145, 94703,
         96094, 95842, 94116, 90068, 94970, 90813, 94404, 94598],
        dtype=object)

numUniqueZips=ploancp_df['ZIPCode'].nunique()
  print("The number of ZIPCodes is",numUniqueZips)
  

The number of ZIPCodes is 467
  

labeled_barplot(ploancp_df, 'ZIPCode', perc=True, n=None)
  

This is also very difficult to visualize and alone it doesn't really give us much information. We will revise it during our bivariate analysis.

Analysis Feature: Family

We visualized Family earlier as a pie chart w/ percentage.

plt.figure(figsize=(6,4), dpi= 60)

  plt.title("Family Size Distribution", fontsize=16)
  sns.boxplot(data=ploancp_df,x='Family')
  plt.show()
  

We see that the median is at 2 members per family, 1 is Q1, 3 is Q3.

Analysis Feature: Education

The description states that 1: undergraduate, 2: graduate, 3: advanced/professional degree.

education_count = ploancp_df['Education'].value_counts()
  education_count
  

1    2096
  3    1501
  2    1403
  Name: Education, dtype: int64

#lets revisit the pie chart for Education, now with the proper labels
  ed_df = ploancp_df['Education'].value_counts()
  edlabels = {'UnderGraduate','Graduate','Advanced/Professional'}
  plt.pie(ed_df,labels = edlabels, autopct=autopct_format(ed_df))
  plt.title("Education Level Distribution")

  plt.show()
  

Is the Education definition correct. Are all customers in the dataset college educated? Was the dataset made to only include college educated customers?

But in any case, lets stick to the categorization made by the bank which states that all customers in this dataset are college educated.

Analysis Feature: Credit Card Use

plt.figure(figsize=(6,4), dpi= 60)
  plt.title("Credit Card Average Usage Distribution", fontsize=16)
  sns.boxplot(data=ploancp_df,x='CCAvg')
  plt.show()
  

There are many outliers. However, these may be possible. During our bivariate analysis, we will take a look at Credit Card average usage again to obtain more meaningful information.

Analysis Feature: Mortgage

#Mortage Distribution
  plt.figure(figsize=(6,4), dpi= 60)
  plt.title("Mortgage Distribution", fontsize=16)
  sns.boxplot(data=ploancp_df,x='Mortgage')
  plt.show()
  

Wouldn't Credit Card usage as a Percentage of Income be more useful? We will take a look at it later during the bivariate analysis.

Analysis Feature: Online, CD Account, Credit Card, Security Account

We have visualized these as countplots and as pie charts. What other insights can we obtain from these from a univariate analysis? Probably not much than we already saw earlier. So let's move on with the bivariate analysis.

Bivariate Analysis

Age vs Personal Loan

distribution_plot_wrt_target(ploancp_df, "Age", "Personal_Loan")
  

Without Loans and w/o outliers: Notice that customers' age 30, 38, 46,52, and 58 are the most common. With Loans and w/o outliers: Notice that 35 and 42 are the most common. What could we determine? Not much.

stacked_barplot(ploancp_df, "Age", "Personal_Loan")
  

Personal_Loan     0    1   All
  Age
  All            4520  480  5000
  34              116   18   134
  30              119   17   136
  36               91   16   107
  63               92   16   108
  35              135   16   151
  33              105   15   120
  52              130   15   145
  29              108   15   123
  54              128   15   143
  43              134   15   149
  42              112   14   126
  56              121   14   135
  65               66   14    80
  44              107   14   121
  50              125   13   138
  45              114   13   127
  46              114   13   127
  26               65   13    78
  32              108   12   120
  57              120   12   132
  38              103   12   115
  27               79   12    91
  48              106   12   118
  61              110   12   122
  53              101   11   112
  51              119   10   129
  60              117   10   127
  58              133   10   143
  49              105   10   115
  47              103   10   113
  59              123    9   132
  28               94    9   103
  62              114    9   123
  55              116    9   125
  64               70    8    78
  41              128    8   136
  40              117    8   125
  37               98    8   106
  31              118    7   125
  39              127    6   133
  24               28    0    28
  25               53    0    53
  66               24    0    24
  67               12    0    12
  23               12    0    12
  ------------------------------------------------------------------------------------------------------------------------
  

From the stacked plot we can see that customers aged 65, 26, 36, 63, 34, 27, 30, 33, 29 are the ones that have highest percentage of personal loans within their own age group.

Now notice that age 65 has the highest % of loans within its total, followed by 26, 36, 63, 34, 27, and 30. So 63+ group may have a larger chance to be loan customers. and also the group between 26-27 and 33-36.

Experience vs Age for Negative Experience

During the univariate analysis we noticed that there are entries with Negative Experience, which of course are wrong values.

We could replace Negative Experience with Median and Mode values, with zeros, or making the values positive and then analyze against other time dependant features like Age and Education.

#case to mode/median
  Experience_mode = ploancp_df['Experience'].mode()
  print("Experience mode is", Experience_mode[0])
  Experience_median = ploancp_df['Experience'].median()
  print("Experience median is",Experience_median)
  

Experience mode is 32
  Experience median is 20.0
  

ploancp_negEx_df['modeX']= ploancp_negEx_df['Experience']-Experience_mode[0]
  ploancp_negEx_df['medianX']= ploancp_negEx_df['Experience']-Experience_median
  

sns.displot(ploancp_negEx_df, x="modeX", kind="kde")
  plt.xlabel("Age")
  plt.title("Age minus Experience Mode")
  plt.show()
  

sns.displot(ploancp_negEx_df, x="medianX", kind="kde")
  plt.xlabel("Age")
  plt.title("Age minus Experience Median")
  plt.show()
  

So it wouldn't make any sense to change Experience=-3, -2, -1 to the mode or median.

• Changing Negative experience to mode will produce Age < Experience!
• Changing Negative experience to median would produce Ages between 3 to 9 years of age!

Therefore, lets rule the Mode/Median Option.

Therefore, lets rule out these 2 options.

How about replacing Experience by 0?

ploancp_x0_df = ploancp_df.loc[ploancp_df['Experience']<0]
  ploancp_x0_df['Experience'] = 0
  ploancp_x0_df
  

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard	Income_bin
89	25	0	113	94303	4	2.300	3	0	0	0	0	0	1	[100..125)
226	24	0	39	94085	2	1.700	2	0	0	0	0	0	0	[25..50)
315	24	0	51	90630	3	0.300	3	0	0	0	0	1	0	[50..75)
451	28	0	48	94132	2	1.750	3	89	0	0	0	1	0	[25..50)
524	24	0	75	93014	4	0.200	1	0	0	0	0	1	0	[50..75)
536	25	0	43	92173	3	2.400	2	176	0	0	0	1	0	[25..50)
540	25	0	109	94010	4	2.300	3	314	0	0	0	1	0	[100..125)
576	25	0	48	92870	3	0.300	3	0	0	0	0	0	1	[25..50)
583	24	0	38	95045	2	1.700	2	0	0	0	0	1	0	[25..50)
597	24	0	125	92835	2	7.200	1	0	0	1	0	0	1	[100..125)
649	25	0	82	92677	4	2.100	3	0	0	0	0	1	0	[75..100)
670	23	0	61	92374	4	2.600	1	239	0	0	0	1	0	[50..75)
686	24	0	38	92612	4	0.600	2	0	0	0	0	1	0	[25..50)
793	24	0	150	94720	2	2.000	1	0	0	0	0	1	0	[125..150)
889	24	0	82	91103	2	1.600	3	0	0	0	0	1	1	[75..100)
909	23	0	149	91709	1	6.330	1	305	0	0	0	0	1	[125..150)
1173	24	0	35	94305	2	1.700	2	0	0	0	0	0	0	[25..50)
1428	25	0	21	94583	4	0.400	1	90	0	0	0	1	0	[0..25)
1522	25	0	101	94720	4	2.300	3	256	0	0	0	0	1	[100..125)
1905	25	0	112	92507	2	2.000	1	241	0	0	0	1	0	[100..125)
2102	25	0	81	92647	2	1.600	3	0	0	0	0	1	1	[75..100)
2430	23	0	73	92120	4	2.600	1	0	0	0	0	1	0	[50..75)
2466	24	0	80	94105	2	1.600	3	0	0	0	0	1	0	[75..100)
2545	25	0	39	94720	3	2.400	2	0	0	0	0	1	0	[25..50)
2618	23	0	55	92704	3	2.400	2	145	0	0	0	1	0	[50..75)
2717	23	0	45	95422	4	0.600	2	0	0	0	0	1	1	[25..50)
2848	24	0	78	94720	2	1.800	2	0	0	0	0	0	0	[75..100)
2876	24	0	80	91107	2	1.600	3	238	0	0	0	0	0	[75..100)
2962	23	0	81	91711	2	1.800	2	0	0	0	0	0	0	[75..100)
2980	25	0	53	94305	3	2.400	2	0	0	0	0	0	0	[50..75)
3076	29	0	62	92672	2	1.750	3	0	0	0	0	0	1	[50..75)
3130	23	0	82	92152	2	1.800	2	0	0	1	0	0	1	[75..100)
3157	23	0	13	94720	4	1.000	1	84	0	0	0	1	0	[0..25)
3279	26	0	44	94901	1	2.000	2	0	0	0	0	0	0	[25..50)
3284	25	0	101	95819	4	2.100	3	0	0	0	0	0	1	[100..125)
3292	25	0	13	95616	4	0.400	1	0	0	1	0	0	0	[0..25)
3394	25	0	113	90089	4	2.100	3	0	0	0	0	1	0	[100..125)
3425	23	0	12	91605	4	1.000	1	90	0	0	0	1	0	[0..25)
3626	24	0	28	90089	4	1.000	3	0	0	0	0	0	0	[25..50)
3796	24	0	50	94920	3	2.400	2	0	0	1	0	0	0	[25..50)
3824	23	0	12	95064	4	1.000	1	0	0	1	0	0	1	[0..25)
3887	24	0	118	92634	2	7.200	1	0	0	1	0	1	0	[100..125)
3946	25	0	40	93117	3	2.400	2	0	0	0	0	1	0	[25..50)
4015	25	0	139	93106	2	2.000	1	0	0	0	0	0	1	[125..150)
4088	29	0	71	94801	2	1.750	3	0	0	0	0	0	0	[50..75)
4116	24	0	135	90065	2	7.200	1	0	0	0	0	1	0	[125..150)
4285	23	0	149	93555	2	7.200	1	0	0	0	0	1	0	[125..150)
4411	23	0	75	90291	2	1.800	2	0	0	0	0	1	1	[50..75)
4481	25	0	35	95045	4	1.000	3	0	0	0	0	1	0	[25..50)
4514	24	0	41	91768	4	1.000	3	0	0	0	0	1	0	[25..50)
4582	25	0	69	92691	3	0.300	3	0	0	0	0	1	0	[50..75)
4957	29	0	50	95842	2	1.750	3	0	0	0	0	0	1	[25..50)

#lets visualize age vs Experience when negative Experience is replaced by 0
  sns.displot(ploancp_x0_df, x="Age", hue="Experience", kind="kde")
  plt.title("Age vs Experience 0")
  plt.show()
  

Notice : 29 years there -> 0 Experience. Also, between 21 to 27 -> 0 Experience, being the largest around 24-25 -> 0 Experience.

ploancp_x0_age_min_df = ploancp_x0_df['Age'].min()
  ploancp_x0_age_median_df = ploancp_x0_df['Age'].median()
  ploancp_x0_age_max_df = ploancp_x0_df['Age'].max()
  print("For negative experience converted to 0, the minimum age is",ploancp_x0_age_min_df,
        ",the mean age is",ploancp_x0_age_median_df,
        ",the maximum age is",ploancp_x0_age_max_df)
  

For negative experience converted to 0, the minimum age is 23 ,the mean age is 24.0 ,the maximum age is 29
  

For Experience = 0

• min: 24, mean: 26, max: 30

For Negative Experience converted to 0

• min: 23, mean: 24m max: 29

Differences between Experience 0 and Converted to 0:

• min: 1, mean: 2, max: 1

How about converting negative experience to positive values, so for example a negative experience of -3 becomes 3?

#make all experience year positive
  ploancp_negEx_df['allPosX']= ploancp_negEx_df['Experience'].abs()
  ploancp_negEx_df
  

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard	modeX	medianX	allPosX
89	25	-1	113	94303	4	2.300	3	0	0	0	0	0	1	-33	-21.000	1
226	24	-1	39	94085	2	1.700	2	0	0	0	0	0	0	-33	-21.000	1
315	24	-2	51	90630	3	0.300	3	0	0	0	0	1	0	-34	-22.000	2
451	28	-2	48	94132	2	1.750	3	89	0	0	0	1	0	-34	-22.000	2
524	24	-1	75	93014	4	0.200	1	0	0	0	0	1	0	-33	-21.000	1
536	25	-1	43	92173	3	2.400	2	176	0	0	0	1	0	-33	-21.000	1
540	25	-1	109	94010	4	2.300	3	314	0	0	0	1	0	-33	-21.000	1
576	25	-1	48	92870	3	0.300	3	0	0	0	0	0	1	-33	-21.000	1
583	24	-1	38	95045	2	1.700	2	0	0	0	0	1	0	-33	-21.000	1
597	24	-2	125	92835	2	7.200	1	0	0	1	0	0	1	-34	-22.000	2
649	25	-1	82	92677	4	2.100	3	0	0	0	0	1	0	-33	-21.000	1
670	23	-1	61	92374	4	2.600	1	239	0	0	0	1	0	-33	-21.000	1
686	24	-1	38	92612	4	0.600	2	0	0	0	0	1	0	-33	-21.000	1
793	24	-2	150	94720	2	2.000	1	0	0	0	0	1	0	-34	-22.000	2
889	24	-2	82	91103	2	1.600	3	0	0	0	0	1	1	-34	-22.000	2
909	23	-1	149	91709	1	6.330	1	305	0	0	0	0	1	-33	-21.000	1
1173	24	-1	35	94305	2	1.700	2	0	0	0	0	0	0	-33	-21.000	1
1428	25	-1	21	94583	4	0.400	1	90	0	0	0	1	0	-33	-21.000	1
1522	25	-1	101	94720	4	2.300	3	256	0	0	0	0	1	-33	-21.000	1
1905	25	-1	112	92507	2	2.000	1	241	0	0	0	1	0	-33	-21.000	1
2102	25	-1	81	92647	2	1.600	3	0	0	0	0	1	1	-33	-21.000	1
2430	23	-1	73	92120	4	2.600	1	0	0	0	0	1	0	-33	-21.000	1
2466	24	-2	80	94105	2	1.600	3	0	0	0	0	1	0	-34	-22.000	2
2545	25	-1	39	94720	3	2.400	2	0	0	0	0	1	0	-33	-21.000	1
2618	23	-3	55	92704	3	2.400	2	145	0	0	0	1	0	-35	-23.000	3
2717	23	-2	45	95422	4	0.600	2	0	0	0	0	1	1	-34	-22.000	2
2848	24	-1	78	94720	2	1.800	2	0	0	0	0	0	0	-33	-21.000	1
2876	24	-2	80	91107	2	1.600	3	238	0	0	0	0	0	-34	-22.000	2
2962	23	-2	81	91711	2	1.800	2	0	0	0	0	0	0	-34	-22.000	2
2980	25	-1	53	94305	3	2.400	2	0	0	0	0	0	0	-33	-21.000	1
3076	29	-1	62	92672	2	1.750	3	0	0	0	0	0	1	-33	-21.000	1
3130	23	-2	82	92152	2	1.800	2	0	0	1	0	0	1	-34	-22.000	2
3157	23	-1	13	94720	4	1.000	1	84	0	0	0	1	0	-33	-21.000	1
3279	26	-1	44	94901	1	2.000	2	0	0	0	0	0	0	-33	-21.000	1
3284	25	-1	101	95819	4	2.100	3	0	0	0	0	0	1	-33	-21.000	1
3292	25	-1	13	95616	4	0.400	1	0	0	1	0	0	0	-33	-21.000	1
3394	25	-1	113	90089	4	2.100	3	0	0	0	0	1	0	-33	-21.000	1
3425	23	-1	12	91605	4	1.000	1	90	0	0	0	1	0	-33	-21.000	1
3626	24	-3	28	90089	4	1.000	3	0	0	0	0	0	0	-35	-23.000	3
3796	24	-2	50	94920	3	2.400	2	0	0	1	0	0	0	-34	-22.000	2
3824	23	-1	12	95064	4	1.000	1	0	0	1	0	0	1	-33	-21.000	1
3887	24	-2	118	92634	2	7.200	1	0	0	1	0	1	0	-34	-22.000	2
3946	25	-1	40	93117	3	2.400	2	0	0	0	0	1	0	-33	-21.000	1
4015	25	-1	139	93106	2	2.000	1	0	0	0	0	0	1	-33	-21.000	1
4088	29	-1	71	94801	2	1.750	3	0	0	0	0	0	0	-33	-21.000	1
4116	24	-2	135	90065	2	7.200	1	0	0	0	0	1	0	-34	-22.000	2
4285	23	-3	149	93555	2	7.200	1	0	0	0	0	1	0	-35	-23.000	3
4411	23	-2	75	90291	2	1.800	2	0	0	0	0	1	1	-34	-22.000	2
4481	25	-2	35	95045	4	1.000	3	0	0	0	0	1	0	-34	-22.000	2
4514	24	-3	41	91768	4	1.000	3	0	0	0	0	1	0	-35	-23.000	3
4582	25	-1	69	92691	3	0.300	3	0	0	0	0	1	0	-33	-21.000	1
4957	29	-1	50	95842	2	1.750	3	0	0	0	0	0	1	-33	-21.000	1

Lets see our results when we convert negative experience to its positive value.

#lets visualize age vs Experience when negative Experience is replaced by its positive value
  sns.displot(ploancp_negEx_df, x="Age", hue="allPosX", kind="kde")
  plt.title("Age vs Experience Positive for negatives")
  plt.show()
  

If we converted the negative experience to Positive values, we notice 29 years -> 1 Experience, and a mostly around 25 years -> 1 Experience. We also notice that around 24 years -> 2 Experience, and at 28 years -> 2 Experience. Notice that around 23-24 years -> 3 Experience.

Does this make sense?

#experience = 1
  ploancp_x1_df = ploancp_df.loc[ploancp_df['Experience']==1]
  x1_median=ploancp_x1_df['Age'].median()
  x1_min=ploancp_x1_df['Age'].min()
  x1_max=ploancp_x1_df['Age'].max()

  ploancp_x1_df
  

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard	Income_bin
0	25	1	49	91107	4	1.600	1	0	0	1	0	0	0	[25..50)
132	31	1	51	90840	2	1.750	3	0	0	0	0	0	0	[50..75)
143	25	1	54	94117	4	1.600	1	0	0	0	0	1	1	[50..75)
165	27	1	43	94706	1	1.500	1	0	0	0	0	1	0	[25..50)
166	25	1	21	95827	3	1.000	2	0	0	0	0	0	0	[0..25)
169	27	1	112	90503	4	2.100	3	0	0	0	0	0	1	[100..125)
170	27	1	138	90250	2	2.000	1	0	0	0	0	1	0	[125..150)
234	26	1	80	95616	1	0.800	2	150	0	0	0	0	0	[75..100)
249	26	1	55	90089	3	2.600	3	113	0	0	0	0	1	[50..75)
263	27	1	74	92121	4	1.800	2	112	0	0	0	1	1	[50..75)
484	25	1	113	95023	2	0.200	1	0	0	0	0	1	1	[100..125)
514	27	1	74	91730	3	0.300	3	0	0	0	0	1	0	[50..75)
732	26	1	85	90064	1	1.900	1	0	0	0	0	1	0	[75..100)
920	27	1	42	94501	3	2.400	2	0	0	0	0	0	0	[25..50)
964	27	1	78	92037	4	2.300	3	157	0	1	0	1	0	[75..100)
1003	25	1	62	94720	4	0.000	1	229	0	0	0	1	0	[50..75)
1065	25	1	113	90401	3	2.500	1	0	0	0	0	0	1	[100..125)
1092	25	1	70	92120	4	2.600	1	0	0	1	0	1	0	[50..75)
1160	28	1	40	95134	1	2.000	2	0	0	1	0	1	0	[25..50)
1204	26	1	190	91604	4	1.300	2	197	1	0	0	1	0	[175..200)
1230	27	1	25	94920	4	0.300	2	0	0	0	0	1	1	[0..25)
1255	27	1	80	95354	2	1.600	3	185	0	0	0	1	1	[75..100)
1262	26	1	53	94720	2	1.600	3	0	0	0	0	1	0	[50..75)
1556	31	1	60	94143	4	4.000	3	244	0	0	0	0	0	[50..75)
1653	26	1	24	96651	2	0.900	3	123	0	0	0	0	1	[0..25)
1690	26	1	102	95521	1	1.900	1	0	0	0	0	0	0	[100..125)
1868	25	1	118	92833	1	5.400	1	0	0	0	0	1	1	[100..125)
1984	26	1	55	92630	4	1.700	2	175	0	0	0	1	0	[50..75)
2192	25	1	13	95814	4	1.000	1	95	0	0	0	0	1	[0..25)
2226	25	1	98	90717	1	5.400	1	0	0	0	0	1	0	[75..100)
2273	27	1	83	91775	4	2.100	3	0	0	0	0	1	1	[75..100)
2360	27	1	85	93302	2	1.600	3	0	0	0	0	0	0	[75..100)
2367	26	1	80	95616	4	0.200	1	0	0	0	0	1	1	[75..100)
2389	27	1	41	90033	1	1.900	3	0	0	0	0	0	1	[25..50)
2446	25	1	70	93010	4	2.600	1	218	0	0	0	1	0	[50..75)
2452	25	1	28	94596	1	1.000	3	0	0	0	0	1	0	[25..50)
2526	26	1	50	95616	4	0.600	2	0	0	0	0	0	0	[25..50)
2527	27	1	43	95120	3	1.100	2	0	0	0	0	0	0	[25..50)
2675	31	1	70	92115	2	1.750	3	0	0	0	1	1	1	[50..75)
2754	26	1	61	93943	4	2.200	1	119	0	0	0	0	0	[50..75)
2815	26	1	48	94019	3	2.600	3	169	0	0	0	0	0	[25..50)
2836	25	1	74	94085	4	2.600	1	204	0	0	0	0	0	[50..75)
2898	27	1	140	91711	1	5.900	2	175	1	1	1	1	0	[125..150)
3010	25	1	72	94301	3	0.800	1	0	0	0	0	1	0	[50..75)
3146	26	1	38	91910	4	1.700	2	0	0	0	0	1	0	[25..50)
3154	27	1	99	94305	1	3.000	3	149	1	0	0	0	1	[75..100)
3339	27	1	141	95135	4	5.100	3	354	1	0	0	0	0	[125..150)
3440	26	1	39	95133	4	0.600	2	0	0	0	0	0	1	[25..50)
3459	26	1	88	94025	2	1.800	2	0	0	0	0	0	0	[75..100)
3486	25	1	20	92806	4	1.000	1	0	0	0	0	0	1	[0..25)
3506	27	1	58	95827	4	1.800	2	154	0	0	1	1	1	[50..75)
3627	27	1	83	90034	2	0.200	1	0	0	0	0	0	1	[75..100)
3708	31	1	74	92116	4	4.000	3	0	0	0	0	0	0	[50..75)
3711	27	1	20	94720	4	0.400	1	99	0	0	0	1	0	[0..25)
3732	26	1	18	92521	2	0.900	3	95	0	0	0	0	0	[0..25)
3845	26	1	54	94061	4	0.600	2	230	0	0	0	0	0	[50..75)
3884	27	1	112	91330	4	2.300	3	402	0	0	0	1	1	[100..125)
4026	27	1	142	92038	3	5.500	1	0	1	0	0	0	0	[125..150)
4235	27	1	91	92173	2	0.200	1	0	0	0	0	1	0	[75..100)
4271	25	1	150	92507	1	6.330	1	0	0	0	0	0	0	[125..150)
4281	28	1	34	94949	4	1.500	2	162	0	0	0	0	1	[25..50)
4305	26	1	54	91709	2	1.600	3	0	0	1	0	0	0	[50..75)
4345	26	1	184	94608	2	4.200	3	577	1	0	1	1	1	[175..200)
4504	27	1	41	93023	4	1.800	3	147	0	0	0	0	0	[25..50)
4507	26	1	8	94550	2	0.900	3	0	0	0	0	0	1	[0..25)
4627	27	1	134	93106	1	1.700	2	307	1	0	0	1	0	[125..150)
4628	27	1	130	94801	3	2.900	2	0	1	0	0	0	0	[125..150)
4669	27	1	64	94501	4	1.800	2	0	0	0	0	1	1	[50..75)
4709	26	1	35	90089	2	1.700	2	119	0	0	0	0	1	[25..50)
4713	25	1	122	93022	2	0.200	1	0	0	0	0	1	0	[100..125)
4888	25	1	121	93106	1	5.400	1	158	0	0	0	1	0	[100..125)
4900	26	1	74	90028	4	2.200	1	0	0	0	0	1	0	[50..75)
4973	31	1	68	95045	4	4.000	3	0	0	0	0	1	0	[50..75)
4984	27	1	98	94043	4	2.300	3	0	0	0	0	0	1	[75..100)

print("For Experience 1, the minimum age is",x1_min,
        ",the mean age is",x1_median,
        ",the maximum age is",x1_max)
  

For Experience 1, the minimum age is 25 ,the mean age is 26.0 ,the maximum age is 31
  

#experience = 2
  ploancp_x2_df = ploancp_df.loc[ploancp_df['Experience']==2]
  x2_median=ploancp_x2_df['Age'].median()
  x2_min=ploancp_x2_df['Age'].min()
  x2_max=ploancp_x2_df['Age'].max()

  ploancp_x2_df
  

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard	Income_bin
58	28	2	93	94065	2	0.200	1	0	0	0	0	0	0	[75..100)
85	27	2	109	94005	4	1.800	3	0	0	0	0	0	0	[100..125)
222	26	2	104	94306	3	2.500	1	0	0	0	0	0	0	[100..125)
277	29	2	30	92126	4	1.000	3	0	0	0	0	0	0	[25..50)
318	27	2	110	95670	4	1.800	3	190	0	0	0	1	0	[100..125)
349	26	2	60	93407	2	3.000	1	132	1	0	0	0	0	[50..75)
397	26	2	48	90503	3	0.700	2	0	0	0	0	1	0	[25..50)
401	29	2	30	95747	4	1.500	2	112	0	0	0	0	1	[25..50)
526	26	2	205	93106	1	6.330	1	271	0	0	0	0	1	[200..225]
533	27	2	101	92807	1	1.900	1	0	0	0	0	0	0	[100..125)
546	27	2	68	94025	3	2.600	3	203	0	1	0	0	0	[50..75)
554	28	2	149	94720	2	7.200	1	0	0	0	0	1	0	[125..150)
608	27	2	55	91910	4	1.700	2	0	0	0	0	1	0	[50..75)
675	29	2	33	91711	1	2.000	2	160	0	0	0	0	0	[25..50)
692	26	2	30	94720	1	1.000	3	111	0	0	0	0	0	[25..50)
770	26	2	172	94551	2	6.900	2	0	1	0	0	1	0	[150..175)
798	29	2	38	93063	1	2.000	2	0	0	0	0	0	0	[25..50)
853	27	2	155	95138	1	0.800	1	0	0	0	0	0	0	[150..175)
864	28	2	10	94080	1	0.100	2	0	0	0	0	1	0	[0..25)
904	28	2	51	90503	4	1.800	2	0	0	1	0	0	0	[50..75)
1059	28	2	11	91203	1	0.100	2	0	0	0	0	1	1	[0..25)
1113	28	2	70	90630	3	0.300	3	0	0	0	0	0	1	[50..75)
1182	28	2	19	94720	4	0.400	1	0	0	0	0	1	1	[0..25)
1213	27	2	78	93943	4	0.200	1	87	0	0	0	0	0	[75..100)
1238	28	2	63	91116	2	1.600	3	0	0	0	0	1	0	[50..75)
1265	32	2	71	95014	2	1.750	3	108	0	0	0	0	0	[50..75)
1275	27	2	92	95616	2	3.100	1	178	0	0	0	1	0	[75..100)
1307	26	2	195	94546	1	6.330	1	0	0	0	0	1	1	[175..200)
1349	26	2	171	93943	3	6.000	2	0	1	0	0	1	0	[150..175)
1350	29	2	29	90266	4	1.500	2	0	0	0	0	0	1	[25..50)
1432	26	2	195	90245	1	6.330	1	0	0	0	0	0	0	[175..200)
1624	28	2	31	90024	2	0.300	2	0	0	1	0	1	0	[25..50)
1722	26	2	72	92647	4	2.600	1	0	0	1	1	1	0	[50..75)
1839	28	2	43	95616	4	1.300	3	0	0	0	0	1	1	[25..50)
1895	26	2	72	95003	4	2.600	1	0	0	0	0	1	0	[50..75)
1931	28	2	140	92122	2	2.000	1	0	0	0	0	1	0	[125..150)
1958	28	2	42	95762	1	1.500	1	0	0	0	0	1	1	[25..50)
1972	28	2	114	94606	4	2.100	3	0	0	0	0	1	0	[100..125)
2000	28	2	22	95670	1	0.100	2	0	0	0	0	1	0	[0..25)
2079	26	2	40	94132	1	1.000	3	0	0	0	0	1	0	[25..50)
2112	27	2	103	93117	1	1.900	1	120	0	0	0	1	0	[100..125)
2123	28	2	9	95014	1	0.100	2	0	0	0	0	1	0	[0..25)
2186	26	2	92	96001	2	0.200	1	0	0	0	0	1	0	[75..100)
2270	26	2	51	92103	4	2.600	1	0	0	0	0	1	0	[50..75)
2304	27	2	170	95818	3	4.700	1	0	1	0	0	1	0	[150..175)
2314	27	2	112	94501	4	1.800	3	0	0	1	0	1	0	[100..125)
2328	27	2	130	92182	3	4.400	1	192	1	0	0	1	0	[125..150)
2387	28	2	51	94720	4	1.800	3	0	0	0	0	0	1	[50..75)
2500	28	2	121	92096	2	2.000	1	341	0	0	0	1	0	[100..125)
2685	28	2	101	90280	4	2.100	3	0	0	0	0	1	0	[100..125)
2807	27	2	129	90009	2	3.300	1	0	0	1	0	0	0	[125..150)
2860	27	2	20	95064	4	0.500	3	0	0	0	0	1	0	[0..25)
2884	28	2	48	93943	4	2.100	3	0	0	0	0	1	1	[25..50)
2897	28	2	34	92161	4	1.300	3	0	0	0	0	0	0	[25..50)
2951	26	2	132	94720	2	2.400	3	0	1	0	0	0	1	[125..150)
3037	27	2	158	95060	3	0.400	2	0	1	0	1	1	0	[150..175)
3040	28	2	33	95814	3	1.000	1	167	0	0	0	1	0	[25..50)
3055	28	2	111	94305	4	2.300	3	0	0	0	0	1	0	[100..125)
3121	28	2	13	91791	4	0.400	1	0	0	0	0	0	1	[0..25)
3228	27	2	45	94305	2	1.700	2	0	0	0	0	0	1	[25..50)
3373	28	2	182	92660	3	7.200	2	442	1	0	1	1	1	[175..200)
3469	26	2	79	95630	2	2.500	1	0	0	0	0	1	0	[75..100)
3494	29	2	31	91330	4	1.500	2	0	0	0	0	0	0	[25..50)
3579	28	2	84	94305	1	2.900	3	102	0	1	1	0	1	[75..100)
3663	26	2	60	94111	4	1.600	1	0	0	0	1	1	1	[50..75)
3751	26	2	12	94591	4	1.000	1	0	0	0	0	1	0	[0..25)
3809	26	2	62	94080	4	1.600	1	0	0	1	0	0	0	[50..75)
3875	26	2	119	95351	2	0.600	1	381	0	0	0	1	1	[100..125)
3885	32	2	69	93943	4	4.000	3	102	0	0	0	1	0	[50..75)
3932	26	2	55	94305	3	0.700	2	0	0	0	0	1	0	[50..75)
4048	27	2	48	90049	2	1.600	3	119	0	1	0	1	0	[25..50)
4085	28	2	53	94609	3	2.400	2	0	0	0	0	1	0	[50..75)
4100	27	2	41	90254	2	1.700	2	0	0	0	0	1	0	[25..50)
4113	28	2	41	93118	3	1.100	2	161	0	0	0	1	0	[25..50)
4185	26	2	82	91950	2	2.500	1	199	0	0	0	0	0	[75..100)
4265	27	2	44	93943	4	0.600	2	0	0	1	1	1	0	[25..50)
4337	26	2	182	93010	2	3.200	2	0	1	0	0	0	0	[175..200)
4362	28	2	55	93940	3	1.100	2	0	0	0	0	0	0	[50..75)
4365	26	2	85	95020	2	2.500	1	0	0	0	0	0	0	[75..100)
4413	29	2	31	91775	4	1.500	2	0	0	0	0	0	1	[25..50)
4508	27	2	85	94117	1	1.900	1	0	0	0	0	1	1	[75..100)
4563	28	2	188	92350	2	4.500	1	0	0	1	0	1	0	[175..200)
4757	26	2	135	94588	1	1.500	1	0	0	0	0	0	0	[125..150)
4769	26	2	20	95064	4	1.000	1	116	0	0	0	0	0	[0..25)
4772	26	2	95	92130	3	0.800	1	0	0	0	0	1	0	[75..100)

print("For Experience 2, the minimum age is",x2_min,
        ",the mean age is",x2_median,
        ",the maximum age is",x2_max)
  

For Experience 2, the minimum age is 26 ,the mean age is 27.0 ,the maximum age is 32
  

#experience = 3
  ploancp_x3_df = ploancp_df.loc[ploancp_df['Experience']==3]
  x3_median=ploancp_x3_df['Age'].median()
  x3_min=ploancp_x3_df['Age'].min()
  x3_max=ploancp_x3_df['Age'].max()

  ploancp_x3_df
  

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard	Income_bin
74	28	3	135	94611	2	3.300	1	0	0	0	0	0	1	[125..150)
177	29	3	65	94132	4	1.800	2	244	0	0	0	0	0	[50..75)
183	29	3	148	92173	3	4.100	1	0	1	0	0	1	0	[125..150)
198	27	3	59	94123	4	0.000	1	90	0	1	0	1	0	[50..75)
202	30	3	68	94306	4	2.000	2	0	0	0	0	1	0	[50..75)
216	27	3	125	95521	2	0.600	1	0	0	0	0	0	0	[100..125)
239	28	3	52	94112	4	1.700	2	0	0	0	0	0	0	[50..75)
272	29	3	45	95023	4	0.200	1	158	0	0	0	1	1	[25..50)
338	29	3	153	93657	2	2.000	1	392	0	0	0	0	0	[150..175)
399	28	3	84	90024	4	0.200	1	0	0	0	0	1	1	[75..100)
421	28	3	115	92333	4	3.100	2	0	1	0	0	0	0	[100..125)
425	28	3	28	90505	4	0.800	1	0	0	0	0	1	0	[25..50)
457	29	3	69	94303	3	0.300	3	0	0	0	0	0	0	[50..75)
552	28	3	52	90024	4	2.200	1	230	0	0	0	1	0	[50..75)
562	28	3	85	94035	1	0.800	2	0	0	0	0	1	1	[75..100)
581	28	3	55	94521	4	2.200	1	0	0	0	0	1	1	[50..75)
590	29	3	39	94612	4	2.100	3	0	0	0	0	1	0	[25..50)
604	28	3	70	90245	4	2.200	1	240	0	0	0	0	1	[50..75)
607	28	3	170	95014	1	0.100	3	0	1	0	0	0	0	[150..175)
731	28	3	90	90066	2	3.300	1	0	0	0	0	1	1	[75..100)
760	29	3	52	92122	3	1.100	2	0	0	0	0	1	0	[50..75)
789	29	3	31	92126	4	0.300	2	0	0	0	0	1	0	[25..50)
799	29	3	39	95051	4	2.100	3	0	0	0	0	1	0	[25..50)
840	27	3	94	92373	2	0.200	1	310	0	0	0	0	1	[75..100)
878	33	3	74	95616	4	4.000	3	0	0	0	0	0	0	[50..75)
899	30	3	172	91302	3	3.400	2	0	1	0	0	0	1	[150..175)
906	29	3	154	94720	2	2.000	1	130	0	0	0	0	0	[150..175)
931	27	3	43	91302	1	1.000	3	0	0	0	0	1	0	[25..50)
995	28	3	45	94305	2	1.600	3	0	0	0	0	1	1	[25..50)
1009	28	3	25	91330	2	0.900	3	140	0	0	0	1	0	[0..25)
1010	27	3	98	95616	2	2.500	1	361	0	1	1	1	1	[75..100)
1019	29	3	30	91745	4	0.300	2	157	0	0	0	0	0	[25..50)
1022	27	3	118	95605	1	3.300	2	0	1	0	0	1	0	[100..125)
1077	29	3	175	90095	3	3.300	3	329	1	0	0	1	0	[150..175)
1083	28	3	65	95014	3	2.600	3	0	0	1	0	0	0	[50..75)
1093	27	3	40	94550	3	0.100	2	111	0	0	0	1	0	[25..50)
1102	29	3	84	95023	1	2.900	3	0	0	0	0	1	0	[75..100)
1176	29	3	103	90049	4	3.400	1	0	1	0	0	1	0	[100..125)
1177	28	3	71	90405	1	3.300	2	149	1	1	1	1	0	[50..75)
1194	29	3	41	94305	4	1.300	3	0	0	0	0	1	0	[25..50)
1286	29	3	50	94010	3	1.100	2	0	0	0	0	0	1	[25..50)
1316	28	3	51	94086	2	1.600	3	123	0	0	0	0	0	[50..75)
1321	27	3	123	95138	1	5.400	1	0	0	0	0	0	0	[100..125)
1377	27	3	109	93023	2	2.500	1	0	0	0	0	1	0	[100..125)
1386	27	3	72	95616	4	0.000	1	0	0	0	0	1	0	[50..75)
1390	29	3	80	94305	4	1.800	2	0	0	0	0	1	1	[75..100)
1424	29	3	92	94539	2	1.300	1	287	0	0	0	1	0	[75..100)
1437	28	3	123	92007	1	0.800	1	146	0	0	0	0	0	[100..125)
1588	29	3	55	95616	3	1.100	2	0	0	0	0	1	0	[50..75)
1618	29	3	29	94720	3	1.000	1	0	0	0	0	1	1	[25..50)
1642	27	3	84	95814	3	1.500	1	0	0	0	0	1	1	[75..100)
1701	29	3	108	94304	4	1.800	2	0	0	0	0	0	0	[100..125)
1711	27	3	201	95819	1	6.330	1	158	0	0	0	1	0	[200..225]
1744	28	3	29	91105	4	0.800	1	135	0	0	0	1	0	[25..50)
1755	28	3	55	92647	4	1.700	2	0	0	0	0	1	1	[50..75)
1778	27	3	32	94710	3	1.000	2	0	0	0	0	0	0	[25..50)
1785	29	3	190	94080	2	4.500	1	0	0	0	0	1	0	[175..200)
1802	29	3	121	92806	2	1.300	1	0	0	0	0	0	0	[100..125)
1811	28	3	11	94534	4	0.500	3	0	0	0	0	0	0	[0..25)
1875	27	3	112	90066	3	2.500	1	389	0	1	0	1	0	[100..125)
1970	27	3	148	92780	1	1.500	1	397	0	0	0	1	1	[125..150)
1975	29	3	113	94132	2	0.200	1	0	0	0	0	1	1	[100..125)
2022	33	3	71	93561	4	1.800	3	236	0	0	0	0	0	[50..75)
2029	30	3	61	92152	4	2.000	2	0	0	0	0	1	0	[50..75)
2052	28	3	120	94080	1	0.800	1	170	0	0	0	0	0	[100..125)
2059	28	3	173	92121	2	6.700	1	222	0	0	0	1	0	[150..175)
2072	29	3	39	95831	4	0.200	1	137	0	0	0	1	1	[25..50)
2146	27	3	30	93108	1	1.000	3	80	0	0	0	0	0	[25..50)
2147	27	3	20	92007	4	1.000	1	0	0	0	0	0	0	[0..25)
2163	33	3	69	92161	4	1.800	3	0	0	0	0	1	0	[50..75)
2164	27	3	104	92007	2	2.500	1	184	0	1	0	1	0	[100..125)
2190	27	3	110	96150	2	0.200	1	294	0	1	0	0	1	[100..125)
2215	28	3	193	94501	3	4.000	2	0	1	0	0	1	0	[175..200)
2261	30	3	150	94305	4	5.000	2	0	1	0	0	1	0	[125..150)
2268	27	3	105	94304	1	3.000	2	0	1	1	0	0	0	[100..125)
2272	27	3	90	91365	3	0.800	1	0	0	0	0	1	0	[75..100)
2276	29	3	172	92093	4	4.400	1	0	1	0	0	0	0	[150..175)
2296	27	3	82	94305	2	0.200	1	0	0	0	0	0	1	[75..100)
2443	28	3	161	92646	4	1.700	3	422	1	0	1	1	1	[150..175)
2489	29	3	41	92626	4	0.200	1	0	0	0	0	1	0	[25..50)
2492	28	3	134	96091	2	3.100	1	0	0	0	0	1	0	[125..150)
2516	28	3	74	94720	3	2.600	3	0	0	0	0	0	0	[50..75)
2741	29	3	49	90266	1	1.500	1	0	0	0	0	0	0	[25..50)
2789	27	3	34	90065	1	0.200	3	0	0	0	0	1	1	[25..50)
2843	27	3	20	95616	4	1.000	1	134	0	0	0	1	1	[0..25)
2853	28	3	54	94550	4	0.600	2	0	0	0	0	1	0	[50..75)
2918	28	3	142	93727	1	0.800	1	0	0	0	0	1	0	[125..150)
2940	27	3	43	90245	3	0.100	2	163	0	0	0	1	0	[25..50)
2963	29	3	41	94588	1	1.900	3	0	0	0	0	1	1	[25..50)
3012	29	3	172	92373	2	4.500	1	415	0	0	0	1	0	[150..175)
3070	28	3	74	91330	2	1.800	2	221	0	1	0	0	0	[50..75)
3180	27	3	103	92121	2	0.600	1	84	0	0	0	0	0	[100..125)
3201	28	3	81	92121	4	0.200	1	0	0	0	0	0	0	[75..100)
3340	29	3	54	94104	4	1.800	3	0	0	0	0	0	0	[50..75)
3350	28	3	95	90245	2	1.800	2	0	0	0	0	0	0	[75..100)
3389	27	3	88	92182	3	0.800	1	238	0	0	0	0	0	[75..100)
3390	29	3	73	94720	3	0.300	3	0	0	0	0	0	0	[50..75)
3453	29	3	31	94709	4	0.300	2	0	0	0	0	1	0	[25..50)
3463	28	3	149	92121	1	0.800	1	0	0	0	0	1	0	[125..150)
3503	29	3	53	95814	4	2.100	3	0	0	0	0	1	0	[50..75)
3583	30	3	33	95112	4	1.500	2	85	0	0	0	0	0	[25..50)
3592	33	3	20	94704	1	0.670	3	0	0	0	0	0	0	[0..25)
3623	28	3	45	91105	4	1.700	2	95	0	0	0	0	0	[25..50)
3667	27	3	59	94590	4	1.600	1	0	0	0	0	1	0	[50..75)
3728	28	3	118	91902	3	2.400	2	161	1	0	0	0	0	[100..125)
3745	27	3	119	90640	1	5.400	1	118	0	0	0	1	0	[100..125)
3776	27	3	135	93108	3	2.700	3	449	1	0	0	0	1	[125..150)
3914	27	3	35	94080	1	1.800	2	0	0	0	0	0	0	[25..50)
3945	29	3	123	92821	3	5.600	3	428	1	0	0	1	0	[100..125)
3968	28	3	78	93108	4	0.200	1	0	0	0	0	1	1	[75..100)
4042	29	3	190	92612	2	4.500	1	246	0	0	0	1	1	[175..200)
4061	33	3	59	91040	2	1.750	3	0	0	0	0	1	0	[50..75)
4098	27	3	75	90032	4	0.000	1	0	0	0	0	0	0	[50..75)
4129	29	3	10	91320	4	0.400	1	87	0	0	0	1	1	[0..25)
4139	29	3	81	95827	1	2.900	3	0	0	0	0	0	0	[75..100)
4179	29	3	91	94122	1	3.400	3	0	1	0	0	0	0	[75..100)
4274	30	3	79	91380	4	2.000	2	0	0	0	0	1	0	[75..100)
4341	28	3	53	94305	2	1.600	3	0	0	0	0	0	0	[50..75)
4351	30	3	32	94132	1	2.000	2	0	0	0	0	1	1	[25..50)
4370	27	3	18	93524	1	0.400	3	0	0	0	0	0	0	[0..25)
4456	29	3	35	94040	2	0.300	1	88	0	0	1	1	1	[25..50)
4515	29	3	49	94305	4	2.100	3	0	0	0	0	0	0	[25..50)
4663	28	3	115	92407	1	1.900	1	200	0	0	0	1	0	[100..125)
4681	27	3	68	95503	4	0.000	1	0	0	0	0	0	1	[50..75)
4688	29	3	69	92093	4	1.800	2	0	0	0	0	1	1	[50..75)
4714	27	3	81	90291	3	1.500	1	307	0	1	1	1	1	[75..100)
4872	27	3	69	94305	3	0.700	2	0	0	0	0	1	1	[50..75)
4952	29	3	53	94005	4	1.800	3	0	0	0	0	1	0	[50..75)
4995	29	3	40	92697	1	1.900	3	0	0	0	0	1	0	[25..50)

print("For Experience 3, the minimum age is",x3_min,
        ",the mean age is",x3_median,
        ",the maximum age is",x3_max)
  

For Experience 3, the minimum age is 27 ,the mean age is 28.0 ,the maximum age is 33
  

#for converted negative experience to positive
  #experience = 3
  ploancp_NegX_3_df = ploancp_negEx_df.loc[ploancp_negEx_df['allPosX']==3]
  NegX3_median=ploancp_NegX_3_df['Age'].median()
  NegX3_min=ploancp_NegX_3_df['Age'].min()
  NegX3_max=ploancp_NegX_3_df['Age'].max()
  print("For Negative Experience -3 to +3, the minimum age is",NegX3_min,
        ",the mean age is",NegX3_median,
        ",the maximum age is",NegX3_max)
  

For Negative Experience -3 to +3, the minimum age is 23 ,the mean age is 23.5 ,the maximum age is 24
  

#for converted negative experience to positive
  #experience = 2
  ploancp_NegX_2_df = ploancp_negEx_df.loc[ploancp_negEx_df['allPosX']==2]
  NegX2_median=ploancp_NegX_2_df['Age'].median()
  NegX2_min=ploancp_NegX_2_df['Age'].min()
  NegX2_max=ploancp_NegX_2_df['Age'].max()
  print("For Negative Experience -2 to +2, the minimum age is",NegX2_min,
        ",the mean age is",NegX2_median,
        ",the maximum age is",NegX2_max)
  

For Negative Experience -2 to +2, the minimum age is 23 ,the mean age is 24.0 ,the maximum age is 28
  

#for converted negative experience to positive
  #experience = 1
  ploancp_NegX_1_df = ploancp_negEx_df.loc[ploancp_negEx_df['allPosX']==1]
  NegX1_median=ploancp_NegX_1_df['Age'].median()
  NegX1_min=ploancp_NegX_1_df['Age'].min()
  NegX1_max=ploancp_NegX_1_df['Age'].max()
  print("For Negative Experience -1 to +1, the minimum age is",NegX1_min,
        ",the mean age is",NegX1_median,
        ",the maximum age is",NegX1_max)
  

For Negative Experience -1 to +1, the minimum age is 23 ,the mean age is 25.0 ,the maximum age is 29
  

Comparing to the case when we converted negative experiences to 0 to the actual positive values:

Actual 1: min: 25, mean: 26, max: 31

Converted 1: min: 23, mean: 25, max: 29

Differences 1-Actual-Converted: min: 2, mean: 1, max: 2

---

Actual 2: min: 26, mean: 27, max: 32

Converted 2: min: 23, mean: 24, max: 28

Differences 2-Actual-Converted: min: 3, mean: 3, max: 4

---

Actual 3: min: 27, mean: 28, max: 33

Converted 3: min: 23, mean: 23.5, max: 24

Differences 2-Actual-Converted: min: 4, mean: 4.5, max: 9

Comparing Converting Negative Experience to 0 or Converting to their positive values, we notice that the minimum, median, and maximum ages for the case of Negative Experience converted to 0 are more similar to the actual Experience =0 of the dataset than the minimum, median, and maximum ages if we would convert negative experiences to their respective positive values and compare them to the actual corresponding positive Experience values of the dataset.

Therefore, we choose to convert negative Experience values to 0.

ploancp_df.loc[ploancp_df['Experience'] < 0, 'Experience'] = 0
  

Experience vs Personal Loan

distribution_plot_wrt_target(ploancp_df, "Experience", "Personal_Loan")
  

Notice that Personal Loan Customers have largest amount of common experience at 5 to 12 years, 18-20 years, and around 30 years. Customers w/o Loans have largest amount of common experiences at 7-8, 18-19, 24-26 years.

Income vs Personal Loan

distribution_plot_wrt_target(ploancp_df, "Income", "Personal_Loan")
  

Personal Loan customers have largest common amount of income 120-140K, and 170-190K. Customers w/o Personal Loans have largest common amount of income 45K.

stacked_barplot(ploancp_df, "Income", "Personal_Loan")
  

Personal_Loan     0    1   All
  Income
  All            4520  480  5000
  130               8   11    19
  182               2   11    13
  158               8   10    18
  135               8   10    18
  179               8    9    17
  141              15    9    24
  154              12    9    21
  123               9    9    18
  184               3    9    12
  142               7    8    15
  131              11    8    19
  129              15    8    23
  172               3    8    11
  173               5    8    13
  170               4    8    12
  180              10    8    18
  115              19    8    27
  125              16    7    23
  164               6    7    13
  188               3    7    10
  83               67    7    74
  114              23    7    30
  161               9    7    16
  122              17    7    24
  133               8    7    15
  132              11    7    18
  191               6    7    13
  134              13    7    20
  111              15    7    22
  190               4    7    11
  145              17    6    23
  140              13    6    19
  178               4    6    10
  118              13    6    19
  185               3    6     9
  165               5    6    11
  168               2    6     8
  169               1    6     7
  183               6    6    12
  120              11    6    17
  139              10    6    16
  113              29    5    34
  119              13    5    18
  99               19    5    24
  138              13    5    18
  155              14    5    19
  195              10    5    15
  174               4    5     9
  175               7    5    12
  152              10    5    15
  153               7    4    11
  181               4    4     8
  103              14    4    18
  93               33    4    37
  108              12    4    16
  101              20    4    24
  194               4    4     8
  192               2    4     6
  193               2    4     6
  143               5    4     9
  149              16    4    20
  171               5    4     9
  160               8    4    12
  159               3    4     7
  128              20    4    24
  148               7    4    11
  162               7    3    10
  112              23    3    26
  110              16    3    19
  124               9    3    12
  105              17    3    20
  104              17    3    20
  102              13    3    16
  109              15    3    18
  95               22    3    25
  150               9    2    11
  94               24    2    26
  163               7    2     9
  91               35    2    37
  98               26    2    28
  89               32    2    34
  121              18    2    20
  85               63    2    65
  144               5    2     7
  65               59    1    60
  71               42    1    43
  69               45    1    46
  100               9    1    10
  60               51    1    52
  189               1    1     2
  73               43    1    44
  151               3    1     4
  201               4    1     5
  64               59    1    60
  202               1    1     2
  81               82    1    83
  92               28    1    29
  90               37    1    38
  75               46    1    47
  82               60    1    61
  84               62    1    63
  203               1    1     2
  33               51    0    51
  198               3    0     3
  31               55    0    55
  30               63    0    63
  29               67    0    67
  28               63    0    63
  25               64    0    64
  24               47    0    47
  23               54    0    54
  22               65    0    65
  224               1    0     1
  21               65    0    65
  20               47    0    47
  19               52    0    52
  200               3    0     3
  218               1    0     1
  18               53    0    53
  204               3    0     3
  15               33    0    33
  199               3    0     3
  14               31    0    31
  13               32    0    32
  12               30    0    30
  205               2    0     2
  11               27    0    27
  10               23    0    23
  32               58    0    58
  48               44    0    44
  34               53    0    53
  35               65    0    65
  9                26    0    26
  88               26    0    26
  80               56    0    56
  79               53    0    53
  78               61    0    61
  74               45    0    45
  72               41    0    41
  70               47    0    47
  68               35    0    35
  63               46    0    46
  62               55    0    55
  61               57    0    57
  59               53    0    53
  58               55    0    55
  55               61    0    61
  54               52    0    52
  53               57    0    57
  52               47    0    47
  51               41    0    41
  50               45    0    45
  49               52    0    52
  45               69    0    69
  44               85    0    85
  43               70    0    70
  42               77    0    77
  41               82    0    82
  40               78    0    78
  39               81    0    81
  38               84    0    84
  8                23    0    23
  ------------------------------------------------------------------------------------------------------------------------
  

Notice that 170K, 182K, 168K, 184K, 172K, 188K, 185K, 192K are the groups that have a largest percentages of Personal Loans within their group.

ZIPCode vs Personal Loan

Where are the majority of customers?

#for the number of accounts
  ziploan = ploancp_df[['ZIPCode','Personal_Loan']]
  ziploan_count = ziploan['ZIPCode'].value_counts()
  ziploan_count_df= pd.DataFrame(ziploan_count)
  ziploan_count_df.reset_index(inplace=True)
  ziploan_count_df = ziploan_count_df.rename(columns={'ZIPCode': 'NoAccounts', 'index': 'ZIPCode'})
  ziploan_count_descending_df = ziploan_count_df.sort_values(['NoAccounts'],ascending=False)
  ziploan_count_descending_df
  

	ZIPCode	NoAccounts
0	94720	169
1	94305	127
2	95616	116
3	90095	71
4	93106	57
...	...	...
460	92694	1
459	90068	1
457	90813	1
456	94404	1
466	94598	1

467 rows × 2 columns

The majority of customers (10%) are in 5 zip codes: 94720, 94305, 95616, 90095, 93106, all corresponding to states in the West coast: California, Oregon, and Washington.

Lets dig into this a bit more.

Does the ZIP code influence whether a personal loan is taken?

How about comparing the number of loans in a ZIP code as a percentage of the total number of loans in the dataset, or the number of accounts in a ZIP code as a percentage of the total number of accounts in the dataset?

ziploan_count_descending_df.head(10)
  

	ZIPCode	NoAccounts
0	94720	169
1	94305	127
2	95616	116
3	90095	71
4	93106	57
5	93943	54
6	92037	54
7	91320	53
8	91711	52
9	94025	52

#for the number of loans
  ziploanpl = ploancp_df[ploancp_df['Personal_Loan']== True]
  number_loans = ziploanpl['Personal_Loan'].value_counts()
  ziploanpl_count = ziploanpl['ZIPCode'].value_counts()
  ziploanpl_count_df = pd.DataFrame(ziploanpl_count)
  ziploanpl_count_df.reset_index(inplace=True)
  ziploanpl_count_df = ziploanpl_count_df.rename(columns={'ZIPCode': 'PLoans', 'index': 'ZIPCode'})
  

#calculating the percentages
  zipLoanTrue = pd.merge(ziploan_count_df, ziploanpl_count_df, on ='ZIPCode')
  zipLoanTrue['PercentLoans'] = (zipLoanTrue['PLoans']/number_loans[1])
  zipLoanTrue['PercentAccounts']= (zipLoanTrue['NoAccounts']/number_accounts)

  #sort in descending order for number of Loans
  zipLoanTrue_descending_df = zipLoanTrue.sort_values(['PLoans'],ascending=False)
  zipLoanTrue_descending_df.head(20)
  

	ZIPCode	NoAccounts	PLoans	PercentLoans	PercentAccounts
0	94720	169	19	0.040	0.034
1	94305	127	13	0.027	0.025
10	92093	51	9	0.019	0.010
16	94304	45	8	0.017	0.009
3	90095	71	8	0.017	0.014
13	90089	46	8	0.017	0.009
20	92182	32	7	0.015	0.006
35	94022	25	6	0.013	0.005
2	95616	116	6	0.013	0.023
18	95051	34	6	0.013	0.007
23	95054	31	5	0.010	0.006
24	95814	30	5	0.010	0.006
6	92037	54	5	0.010	0.011
12	90245	50	5	0.010	0.010
73	94928	15	5	0.010	0.003
43	91380	22	5	0.010	0.004
44	92612	22	4	0.008	0.004
36	95060	25	4	0.008	0.005
32	93407	26	4	0.008	0.005
30	95039	26	4	0.008	0.005

#sort in descending order for % of accounts out of total number
  zipLoanTrue_descending_df = zipLoanTrue.sort_values(['PercentAccounts'],ascending=False)
  zipLoanTrue_descending_df.head(50)
  

	ZIPCode	NoAccounts	PLoans	PercentLoans	PercentAccounts
0	94720	169	19	0.040	0.034
1	94305	127	13	0.027	0.025
2	95616	116	6	0.013	0.023
3	90095	71	8	0.017	0.014
4	93106	57	4	0.008	0.011
5	93943	54	4	0.008	0.011
6	92037	54	5	0.010	0.011
7	91320	53	2	0.004	0.011
8	91711	52	4	0.008	0.010
9	94025	52	4	0.008	0.010
10	92093	51	9	0.019	0.010
11	90024	50	1	0.002	0.010
12	90245	50	5	0.010	0.010
13	90089	46	8	0.017	0.009
14	91330	46	3	0.006	0.009
16	94304	45	8	0.017	0.009
15	92121	45	3	0.006	0.009
17	94143	37	3	0.006	0.007
18	95051	34	6	0.013	0.007
19	94608	34	1	0.002	0.007
20	92182	32	7	0.015	0.006
21	92028	32	4	0.008	0.006
22	92521	32	3	0.006	0.006
23	95054	31	5	0.010	0.006
24	95814	30	5	0.010	0.006
25	95014	29	4	0.008	0.006
26	94542	27	3	0.006	0.005
27	94301	27	2	0.004	0.005
31	95819	26	1	0.002	0.005
32	93407	26	4	0.008	0.005
28	95064	26	3	0.006	0.005
30	95039	26	4	0.008	0.005
29	94501	26	2	0.004	0.005
33	94105	25	1	0.002	0.005
34	91107	25	3	0.006	0.005
35	94022	25	6	0.013	0.005
36	95060	25	4	0.008	0.005
37	94303	25	2	0.004	0.005
38	93117	24	1	0.002	0.005
39	94596	24	4	0.008	0.005
40	93555	23	2	0.004	0.005
41	94080	23	1	0.002	0.005
42	95521	23	1	0.002	0.005
45	92717	22	3	0.006	0.004
43	91380	22	5	0.010	0.004
44	92612	22	4	0.008	0.004
46	92647	21	2	0.004	0.004
47	94110	21	1	0.002	0.004
48	91768	21	2	0.004	0.004
49	90034	20	1	0.002	0.004

We can focus in zip codes that have more than 1 personal loans

zipLoanTrue_descending_loan2_df = zipLoanTrue_descending_df.loc[zipLoanTrue_descending_df['PLoans']>=2]
  zipLoanTrue_descending_loan2_df
  

	ZIPCode	NoAccounts	PLoans	PercentLoans	PercentAccounts
0	94720	169	19	0.040	0.034
1	94305	127	13	0.027	0.025
2	95616	116	6	0.013	0.023
3	90095	71	8	0.017	0.014
4	93106	57	4	0.008	0.011
5	93943	54	4	0.008	0.011
6	92037	54	5	0.010	0.011
7	91320	53	2	0.004	0.011
8	91711	52	4	0.008	0.010
9	94025	52	4	0.008	0.010
10	92093	51	9	0.019	0.010
12	90245	50	5	0.010	0.010
13	90089	46	8	0.017	0.009
14	91330	46	3	0.006	0.009
16	94304	45	8	0.017	0.009
15	92121	45	3	0.006	0.009
17	94143	37	3	0.006	0.007
18	95051	34	6	0.013	0.007
20	92182	32	7	0.015	0.006
21	92028	32	4	0.008	0.006
22	92521	32	3	0.006	0.006
23	95054	31	5	0.010	0.006
24	95814	30	5	0.010	0.006
25	95014	29	4	0.008	0.006
26	94542	27	3	0.006	0.005
27	94301	27	2	0.004	0.005
32	93407	26	4	0.008	0.005
28	95064	26	3	0.006	0.005
30	95039	26	4	0.008	0.005
29	94501	26	2	0.004	0.005
34	91107	25	3	0.006	0.005
35	94022	25	6	0.013	0.005
36	95060	25	4	0.008	0.005
37	94303	25	2	0.004	0.005
39	94596	24	4	0.008	0.005
40	93555	23	2	0.004	0.005
45	92717	22	3	0.006	0.004
43	91380	22	5	0.010	0.004
44	92612	22	4	0.008	0.004
46	92647	21	2	0.004	0.004
48	91768	21	2	0.004	0.004
50	92122	19	2	0.004	0.004
51	92697	19	2	0.004	0.004
52	90025	19	2	0.004	0.004
53	95747	19	2	0.004	0.004
57	90291	18	3	0.006	0.004
59	94122	18	2	0.004	0.004
58	93940	18	2	0.004	0.004
56	94309	18	2	0.004	0.004
55	94709	18	2	0.004	0.004
54	90840	18	2	0.004	0.004
63	94583	17	2	0.004	0.003
61	90630	17	2	0.004	0.003
60	93023	17	3	0.006	0.003
68	92677	16	3	0.006	0.003
69	95136	16	2	0.004	0.003
71	92126	16	2	0.004	0.003
78	94061	15	2	0.004	0.003
77	91604	15	2	0.004	0.003
73	94928	15	5	0.010	0.003
79	92152	14	3	0.006	0.003
81	94590	14	2	0.004	0.003
84	90064	14	2	0.004	0.003
86	94111	14	2	0.004	0.003
91	92007	13	4	0.008	0.003
88	94704	13	2	0.004	0.003
94	91355	12	4	0.008	0.002
97	93955	12	4	0.008	0.002
98	93108	12	3	0.006	0.002
100	95818	12	2	0.004	0.002
108	94801	11	2	0.004	0.002
105	95008	11	2	0.004	0.002
103	92008	11	2	0.004	0.002
101	94306	11	2	0.004	0.002
124	93014	10	2	0.004	0.002
123	91360	10	2	0.004	0.002
122	92173	10	2	0.004	0.002
120	94086	10	2	0.004	0.002
119	95032	10	3	0.006	0.002
114	91902	10	2	0.004	0.002
112	92646	10	3	0.006	0.002
111	90405	10	2	0.004	0.002
134	91302	9	3	0.006	0.002
133	94131	9	2	0.004	0.002
132	94102	9	2	0.004	0.002
129	92660	9	2	0.004	0.002
128	91103	9	2	0.004	0.002
127	90049	9	3	0.006	0.002
126	95605	9	2	0.004	0.002
145	93561	8	2	0.004	0.002
144	92626	8	2	0.004	0.002
139	91101	8	2	0.004	0.002
140	90065	8	2	0.004	0.002
138	92672	8	2	0.004	0.002
160	95138	7	2	0.004	0.001
170	92220	7	2	0.004	0.001
164	91423	7	2	0.004	0.001
159	92333	7	2	0.004	0.001
155	94114	7	2	0.004	0.001
171	92056	6	3	0.006	0.001
177	94553	6	2	0.004	0.001
179	90502	6	2	0.004	0.001
202	93022	5	2	0.004	0.001
210	94108	4	2	0.004	0.001
208	90059	4	2	0.004	0.001
207	94705	4	2	0.004	0.001
222	96008	3	2	0.004	0.001
221	95135	3	2	0.004	0.001

ZIP Code 94720 has 19 PL, 94305 has 13 PL, 92093 has 9 PL, 90095, 94304, 90089 have 8 PL, 92182 has 7 PL, 94022, 95051, 95616 have 6 PL, 95054, 95814, 92037, 90245, 94928,91380 have 5 PL

ZIP Code 94720 is the UC Berkeley campus.

ZIP Code 94305 contains the Stanford Univ campus.

ZIP Code 92093 contains the UC Santa Clara and U San Diego campus

ZIP Code 90095: contains UCLA

ZIP Code 94304: Palo Alto, CA close to Stanford

ZIP Code 90089: contains USC or very near to USC

ZIP Code 92182: contains San Diego State Univ.

ZIP Code 94022: this zip code also has many schools, like Palo Alto University, Foothill College, etc.

ZIP Code 95051: Mission College, Santa Clara Univ, etc.

ZIP Code 95616: UC at Davis

ZIP Code 95054: Santa Clara, CA

ZIP Code 95814: Sacramento, CA

ZIP Code 92037: La Jolla, CA

ZIP Code 90245: El Segundo, CA

ZIP Code 94928,

ZIP Code 91380

Is this a clue that college students take out loans, which would mean that customers that are in college zip codes, are more likely than to take a loan than customers that are not in college?

We can later take a look at the age group to see if these match a profile of a student age (beware of non-traditional students). However because many of the ZIP codes are in school areas, there could be some kind of association with the schools (faculty, or be a post-graduate student, or work there for example or even be a non-traditional student). They also seem to be mostly experienced, have large incomes.

Family vs Personal Loan

distribution_plot_wrt_target(ploancp_df, "Family", "Personal_Loan")
  

Notice that customers with 3 and 4 family members have more Personal Loans.

stacked_barplot(ploancp_df, "Family", "Personal_Loan")
  

Personal_Loan     0    1   All
  Family
  All            4520  480  5000
  4              1088  134  1222
  3               877  133  1010
  1              1365  107  1472
  2              1190  106  1296
  ------------------------------------------------------------------------------------------------------------------------
  

Family size 4 have the largest amount of Personal Loan and the highest percentage within its group. Family size 3 have the second largest percentage within its group.

Credit Card Average Expenses vs Personal Loan

Credit Card Average as Year Income Percentage vs Personal Loan

#lets create a new column to represent the % of CCAVg per month over Average monthly Income.
  ploancp_df['CCAvg_Inc%']=(ploancp_df['CCAvg']*100/(ploancp_df['Income']/12))
  ploancp_df['CCAvg_Inc%']
  

0       39.184
  1       52.941
  2      109.091
  3       32.400
  4       26.667
           ...
  4995    57.000
  4996    32.000
  4997    15.000
  4998    12.245
  4999    11.566
  Name: CCAvg_Inc%, Length: 5000, dtype: float64

Credit card usage as a percentage of Income is more meaningful than an absolute value.

ploancp_df['CCAvg_Inc%'].value_counts()
  ploancp_df['CCAvg_Inc%'].max()
  ploancp_df['CCAvg_Inc%'].min()
  

0.0

plt.figure(figsize=(6,4), dpi= 60)
  plt.title("Credit Card Average Usage % of Income Distribution ", fontsize=16)
  sns.boxplot(data=ploancp_df,x='CCAvg_Inc%')
  plt.show()
  

Notice the outliers above 90% monthly income expense in monthly credit card usage.

So lets drop the credit card Usage CCAvg field.

We should however later take a look in the bivariate analysis, of how Credit card usage looks against Personal Loans.

ploancp_df.drop('CCAvg', axis=1, inplace=True)
  

Maybe we can create bins to simplify our analysis?

Lets create bins of every 10%

bins = [-0.1, 0, 10, 20, 30,40, 50, 60,70,80, 90, 100, 110, 120, 130, 140, 150]
  labels = [0, 10, 20, 30,40, 50, 60,70,80, 90, 100, 110, 120, 130, 140, 150]
  ploancp_df['CCAvg_Inc%_bin'] = pd.cut(ploancp_df['CCAvg_Inc%'], bins=bins, labels=labels)
  print (ploancp_df)
  

      Age  Experience  Income ZIPCode  Family Education  Mortgage  \
  0      25           1      49   91107       4         1         0
  1      45          19      34   90089       3         1         0
  2      39          15      11   94720       1         1         0
  3      35           9     100   94112       1         2         0
  4      35           8      45   91330       4         2         0
  ...   ...         ...     ...     ...     ...       ...       ...
  4995   29           3      40   92697       1         3         0
  4996   30           4      15   92037       4         1        85
  4997   63          39      24   93023       2         3         0
  4998   65          40      49   90034       3         2         0
  4999   28           4      83   92612       3         1         0

       Personal_Loan Securities_Account CD_Account Online CreditCard Income_bin  \
  0                0                  1          0      0          0   [25..50)
  1                0                  1          0      0          0   [25..50)
  2                0                  0          0      0          0    [0..25)
  3                0                  0          0      0          0  [75..100)
  4                0                  0          0      0          1   [25..50)
  ...            ...                ...        ...    ...        ...        ...
  4995             0                  0          0      1          0   [25..50)
  4996             0                  0          0      1          0    [0..25)
  4997             0                  0          0      0          0    [0..25)
  4998             0                  0          0      1          0   [25..50)
  4999             0                  0          0      1          1  [75..100)

        CCAvg_Inc% CCAvg_Inc%_bin
  0         39.184             40
  1         52.941             60
  2        109.091            110
  3         32.400             40
  4         26.667             30
  ...          ...            ...
  4995      57.000             60
  4996      32.000             40
  4997      15.000             20
  4998      12.245             20
  4999      11.566             20

  [5000 rows x 15 columns]
  

ploancp_df.info()
  

<class 'pandas.core.frame.DataFrame'>
  RangeIndex: 5000 entries, 0 to 4999
  Data columns (total 15 columns):
   #   Column              Non-Null Count  Dtype
  ---  ------              --------------  -----
   0   Age                 5000 non-null   int64
   1   Experience          5000 non-null   int64
   2   Income              5000 non-null   int64
   3   ZIPCode             5000 non-null   object
   4   Family              5000 non-null   int64
   5   Education           5000 non-null   object
   6   Mortgage            5000 non-null   int64
   7   Personal_Loan       5000 non-null   object
   8   Securities_Account  5000 non-null   object
   9   CD_Account          5000 non-null   object
   10  Online              5000 non-null   object
   11  CreditCard          5000 non-null   object
   12  Income_bin          5000 non-null   category
   13  CCAvg_Inc%          5000 non-null   float64
   14  CCAvg_Inc%_bin      5000 non-null   category
  dtypes: category(2), float64(1), int64(5), object(7)
  memory usage: 518.7+ KB
  

distribution_plot_wrt_target(ploancp_df, "CCAvg_Inc%","Personal_Loan")
  

Credit Card Average as year Income Percentage Bins vs Personal Loan

distribution_plot_wrt_target(ploancp_df, "CCAvg_Inc%_bin","Personal_Loan")
  

stacked_barplot(ploancp_df, "CCAvg_Inc%_bin", "Personal_Loan")
  

Personal_Loan      0    1   All
  CCAvg_Inc%_bin
  All             4520  480  5000
  50               590  111   701
  60               526   88   614
  30               785   80   865
  40               764   77   841
  10               562   60   622
  20               727   60   787
  70               227    3   230
  0                105    1   106
  80               146    0   146
  90                48    0    48
  100               25    0    25
  110                7    0     7
  120                3    0     3
  140                4    0     4
  150                1    0     1
  ------------------------------------------------------------------------------------------------------------------------
  

Notice that customers with average 40-50% credit card usage as % of their monthly income have more Personal Loans than the rest.

Customers that have 50%, 60% of Credit card expenses as a % of their monthly income, also have the largest % of Personal Loans within their group.

Education vs Personal Loan

distribution_plot_wrt_target(ploancp_df, "Education", "Personal_Loan")
  

stacked_barplot(ploancp_df, "Education", "Personal_Loan")
  

Personal_Loan     0    1   All
  Education
  All            4520  480  5000
  3              1296  205  1501
  2              1221  182  1403
  1              2003   93  2096
  ------------------------------------------------------------------------------------------------------------------------
  

On Education, we can see that Customers with Advanced/Professional education take more Personal Loans than customers w/ graduate education. Customers with Graduate education take more loans than customers with undegraduate education.

Assuming that an Advanced Degree/Professional Degree is more education than a Graduate Degree, we can confidently claim that the more Education, the more Loans customers get.

Mortgage vs Personal Loan

How about Mortgage as a % of Income?

#lets create a new column to represent the % of Mortage over yearly Income.
  ploancp_df['Mortgage_Inc%']=(ploancp_df['Mortgage']*100/ploancp_df['Income'])
  ploancp_df['Mortgage_Inc%']
  

0        0.000
  1        0.000
  2        0.000
  3        0.000
  4        0.000
           ...
  4995     0.000
  4996   566.667
  4997     0.000
  4998     0.000
  4999     0.000
  Name: Mortgage_Inc%, Length: 5000, dtype: float64

#Mortage Distribution as a % of Yearly Income
  plt.figure(figsize=(6,4), dpi= 60)
  plt.title("Total Mortgage as % of Yearly Income Distribution", fontsize=16)
  sns.boxplot(data=ploancp_df,x='Mortgage_Inc%')
  plt.show()
  

Because Mortgages are spread through long periods of 20-30 years, the % will be very high. However, what if we dare to see the mortgage as a % of yearly income matched to mortgage yearly cost (simple w/o annuities, amortizations, etc)?

According to Google, the average length of a Mortgage is 30 years.

#lets create a new column to represent the % of yearly Mortage over yearly Income.
  ploancp_df['year_Mortgage_Inc%']=((ploancp_df['Mortgage']/30)*100/ploancp_df['Income'])
  ploancp_df['year_Mortgage_Inc%']
  

0       0.000
  1       0.000
  2       0.000
  3       0.000
  4       0.000
          ...
  4995    0.000
  4996   18.889
  4997    0.000
  4998    0.000
  4999    0.000
  Name: year_Mortgage_Inc%, Length: 5000, dtype: float64

#Yearly Mortage Distribution as a % of Yearly Income
  plt.figure(figsize=(6,4), dpi= 60)
  plt.title("Yearly Mortgage Amortization as % Yearly Income Distribution", fontsize=16)
  sns.boxplot(data=ploancp_df,x='year_Mortgage_Inc%')
  plt.show()
  

Mortgage as Yearly Income Percentage vs Personal Loan

distribution_plot_wrt_target(ploancp_df, "year_Mortgage_Inc%","Personal_Loan")
  

Mortgage as Yearly Income Percentage Bins vs Personal Loan

bins = [-0.1,0, 5, 10, 15, 20,25, 30, 35,40,45]
  labels = [0, 5, 10, 15, 20,25, 30, 35,40,45]
  ploancp_df['year_Mortage_Inc%_bin'] = pd.cut(ploancp_df['year_Mortgage_Inc%'], bins=bins, labels=labels)
  print (ploancp_df)
  

      Age  Experience  Income ZIPCode  Family Education  Mortgage  \
  0      25           1      49   91107       4         1         0
  1      45          19      34   90089       3         1         0
  2      39          15      11   94720       1         1         0
  3      35           9     100   94112       1         2         0
  4      35           8      45   91330       4         2         0
  ...   ...         ...     ...     ...     ...       ...       ...
  4995   29           3      40   92697       1         3         0
  4996   30           4      15   92037       4         1        85
  4997   63          39      24   93023       2         3         0
  4998   65          40      49   90034       3         2         0
  4999   28           4      83   92612       3         1         0

       Personal_Loan Securities_Account CD_Account Online CreditCard Income_bin  \
  0                0                  1          0      0          0   [25..50)
  1                0                  1          0      0          0   [25..50)
  2                0                  0          0      0          0    [0..25)
  3                0                  0          0      0          0  [75..100)
  4                0                  0          0      0          1   [25..50)
  ...            ...                ...        ...    ...        ...        ...
  4995             0                  0          0      1          0   [25..50)
  4996             0                  0          0      1          0    [0..25)
  4997             0                  0          0      0          0    [0..25)
  4998             0                  0          0      1          0   [25..50)
  4999             0                  0          0      1          1  [75..100)

        CCAvg_Inc% CCAvg_Inc%_bin  Mortgage_Inc%  year_Mortgage_Inc%  \
  0         39.184             40          0.000               0.000
  1         52.941             60          0.000               0.000
  2        109.091            110          0.000               0.000
  3         32.400             40          0.000               0.000
  4         26.667             30          0.000               0.000
  ...          ...            ...            ...                 ...
  4995      57.000             60          0.000               0.000
  4996      32.000             40        566.667              18.889
  4997      15.000             20          0.000               0.000
  4998      12.245             20          0.000               0.000
  4999      11.566             20          0.000               0.000

       year_Mortage_Inc%_bin
  0                        0
  1                        0
  2                        0
  3                        0
  4                        0
  ...                    ...
  4995                     0
  4996                    20
  4997                     0
  4998                     0
  4999                     0

  [5000 rows x 18 columns]
  

#ploancp_df['year_Mortage_Inc%_bin']
  ploancp_df['year_Mortage_Inc%_bin'] = ploancp_df['year_Mortage_Inc%_bin'].astype('int')
  

distribution_plot_wrt_target(ploancp_df, "year_Mortage_Inc%_bin","Personal_Loan")
  

sns.countplot(ploancp_df["year_Mortage_Inc%_bin"])
  plt.title("Mortgage Distribution as Yearly Income % - 5% bins")
  plt.show()
  

We notice that there is a customer that pays more than 40% of his/her yearly income in mortage. But we will analyze this in the bivariate analysis better.

Now Lets drop the columns 'Mortgage' and 'Mortgage_Inc%"

ploancp_df.drop(['Mortgage','Mortgage_Inc%'], axis=1, inplace=True)
  

#lets look at the mortage
  ploancp_df['year_Mortgage_Inc%'].value_counts()
  

0.000     3462
  10.000       5
  14.545       5
  11.429       4
  4.667        4
            ...
  5.805        1
  10.000       1
  24.524       1
  10.397       1
  18.889       1
  Name: year_Mortgage_Inc%, Length: 1392, dtype: int64

ploancp_df['year_Mortgage_Inc%'].max()
  ploancp_df['year_Mortgage_Inc%'].min()
  

0.0

lets bin the mortages

stacked_barplot(ploancp_df, "year_Mortage_Inc%_bin", "Personal_Loan")
  

Personal_Loan             0    1   All
  year_Mortage_Inc%_bin
  All                    4520  480  5000
  0                      3150  312  3462
  10                      504   77   581
  5                       149   62   211
  15                      484   29   513
  20                      143    0   143
  25                       43    0    43
  30                       26    0    26
  35                       12    0    12
  40                        7    0     7
  45                        2    0     2
  ------------------------------------------------------------------------------------------------------------------------
  

Notice that customers that have mortgages and that mortage is 5%-10% of their yearly income have the largest % of personal loans

Securities Account vs Personal Loan

distribution_plot_wrt_target(ploancp_df, "Securities_Account", "Personal_Loan")
  

stacked_barplot(ploancp_df, "Securities_Account", "Personal_Loan")
  

Personal_Loan          0    1   All
  Securities_Account
  All                 4520  480  5000
  0                   4058  420  4478
  1                    462   60   522
  ------------------------------------------------------------------------------------------------------------------------
  

Customers with Securities Account have a slightly largest % of personal Loans within their group.

CD Account vs Personal Loan

distribution_plot_wrt_target(ploancp_df, "CD_Account", "Personal_Loan")
  

stacked_barplot(ploancp_df, "CD_Account", "Personal_Loan")
  

Personal_Loan     0    1   All
  CD_Account
  All            4520  480  5000
  0              4358  340  4698
  1               162  140   302
  ------------------------------------------------------------------------------------------------------------------------
  

Customers with CD Accounts also tend to have more Personal Loans % within their group than customers w/o a CD Account.

Online vs Personal Loan

distribution_plot_wrt_target(ploancp_df, "Online", "Personal_Loan")
  

stacked_barplot(ploancp_df, "Online", "Personal_Loan")
  

Personal_Loan     0    1   All
  Online
  All            4520  480  5000
  1              2693  291  2984
  0              1827  189  2016
  ------------------------------------------------------------------------------------------------------------------------
  

Customers that use Online access and customers that don't use Online access are very similar in the amount of Personal Loans.

Credit Card vs Personal Loan

distribution_plot_wrt_target(ploancp_df, "CreditCard", "Personal_Loan")
  

stacked_barplot(ploancp_df, "CreditCard", "Personal_Loan")
  

Personal_Loan     0    1   All
  CreditCard
  All            4520  480  5000
  0              3193  337  3530
  1              1327  143  1470
  ------------------------------------------------------------------------------------------------------------------------
  

There are more customers w/o a Credit Card that have a Personal Loans than those with a credit card. However, percentage wise within their group, a larger % within their group of Customers that have Credit cards than those w/o credit cards.

Second Data Preparation

ploancp_df.info()
  

<class 'pandas.core.frame.DataFrame'>
  RangeIndex: 5000 entries, 0 to 4999
  Data columns (total 16 columns):
   #   Column                 Non-Null Count  Dtype
  ---  ------                 --------------  -----
   0   Age                    5000 non-null   int64
   1   Experience             5000 non-null   int64
   2   Income                 5000 non-null   int64
   3   ZIPCode                5000 non-null   object
   4   Family                 5000 non-null   int64
   5   Education              5000 non-null   object
   6   Personal_Loan          5000 non-null   object
   7   Securities_Account     5000 non-null   object
   8   CD_Account             5000 non-null   object
   9   Online                 5000 non-null   object
   10  CreditCard             5000 non-null   object
   11  Income_bin             5000 non-null   category
   12  CCAvg_Inc%             5000 non-null   float64
   13  CCAvg_Inc%_bin         5000 non-null   category
   14  year_Mortgage_Inc%     5000 non-null   float64
   15  year_Mortage_Inc%_bin  5000 non-null   int64
  dtypes: category(2), float64(2), int64(5), object(7)
  memory usage: 557.8+ KB
  

We can drop year_Mortage_Inc%_bin, CCAvg_Inc%_bin, Income_bin.

ploancp_df.drop(['year_Mortage_Inc%_bin', 'CCAvg_Inc%_bin', 'Income_bin'], axis=1,inplace=True)
  

ploancp_df.info()
  

<class 'pandas.core.frame.DataFrame'>
  RangeIndex: 5000 entries, 0 to 4999
  Data columns (total 13 columns):
   #   Column              Non-Null Count  Dtype
  ---  ------              --------------  -----
   0   Age                 5000 non-null   int64
   1   Experience          5000 non-null   int64
   2   Income              5000 non-null   int64
   3   ZIPCode             5000 non-null   object
   4   Family              5000 non-null   int64
   5   Education           5000 non-null   object
   6   Personal_Loan       5000 non-null   object
   7   Securities_Account  5000 non-null   object
   8   CD_Account          5000 non-null   object
   9   Online              5000 non-null   object
   10  CreditCard          5000 non-null   object
   11  CCAvg_Inc%          5000 non-null   float64
   12  year_Mortgage_Inc%  5000 non-null   float64
  dtypes: float64(2), int64(4), object(7)
  memory usage: 507.9+ KB
  

numerical_col = ploancp_df.select_dtypes(include=np.number).columns.tolist()
  plt.figure(figsize=(20, 30))

  for i, variable in enumerate(numerical_col):
      plt.subplot(5, 4, i + 1)
      plt.boxplot(ploancp_df[variable], whis=1.5)
      plt.tight_layout()
      plt.title(variable)

  plt.show()
  

ploancp_df.dtypes
  

Age                     int64
  Experience              int64
  Income                  int64
  ZIPCode                object
  Family                  int64
  Education              object
  Personal_Loan          object
  Securities_Account     object
  CD_Account             object
  Online                 object
  CreditCard             object
  CCAvg_Inc%            float64
  year_Mortgage_Inc%    float64
  dtype: object

Logistic Regression Modelling

Should we hot-encode or dummy any features?

1. Personal Loan, Securities Account, CD_Account, Online, CreditCard are ok as they are since they are either a 1 for Yes, and 0 for No.

2. The big issue is ZIPCodes. Although there are over 400 zipcodes, how else could we introduce ZIPcodes? Will we be overfitting the model? Should we stick to dimension reduction by binning zip codes?

However, if we bin zip codes, like for example, regions or states, we may lose data.

We will aim to hot encode zip codes.

First, lets convert our Objects to integers 0 or 1 w/o considering ZIPCode for the time being.

cols_num2cat = ['Education','Personal_Loan','Securities_Account','CD_Account','Online','CreditCard','CCAvg_Inc%','year_Mortgage_Inc%']
  ploancp_df[cols_num2cat] = ploancp_df[cols_num2cat].astype('int')
  

Lets take a look at the correlation between numerical features.

fig, ax = plt.subplots(figsize=(12,10))
  ax=sns.heatmap(ploancp_df.corr(), cmap="YlGnBu", annot=True)
  

Correlation Levels for Features vs Personal Loan Medium Correlation : Income and Personal Loan (0.5) Medium Low Correlation: CD_Account and Personal Loan (0.32) Low Correlation : Education and Personal Loan (0.14)

Now lets hot-encode (dummy) the ZIP Codes.

ploancp_df= pd.get_dummies(ploancp_df, columns = ['ZIPCode'])
  

ploancp_df.head()
  

	Age	Experience	Income	Family	Education	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard	CCAvg_Inc%	year_Mortgage_Inc%	ZIPCode_90005	ZIPCode_90007	ZIPCode_90009	ZIPCode_90011	ZIPCode_90016	ZIPCode_90018	ZIPCode_90019	ZIPCode_90024	ZIPCode_90025	ZIPCode_90027	ZIPCode_90028	ZIPCode_90029	ZIPCode_90032	ZIPCode_90033	ZIPCode_90034	ZIPCode_90035	ZIPCode_90036	ZIPCode_90037	ZIPCode_90041	ZIPCode_90044	ZIPCode_90045	ZIPCode_90048	ZIPCode_90049	ZIPCode_90057	ZIPCode_90058	ZIPCode_90059	ZIPCode_90064	ZIPCode_90065	ZIPCode_90066	ZIPCode_90068	ZIPCode_90071	ZIPCode_90073	ZIPCode_90086	ZIPCode_90089	ZIPCode_90095	ZIPCode_90210	ZIPCode_90212	ZIPCode_90230	ZIPCode_90232	ZIPCode_90245	ZIPCode_90250	ZIPCode_90254	ZIPCode_90266	ZIPCode_90272	ZIPCode_90274	ZIPCode_90275	ZIPCode_90277	ZIPCode_90280	ZIPCode_90291	ZIPCode_90304	ZIPCode_90401	ZIPCode_90404	ZIPCode_90405	ZIPCode_90502	ZIPCode_90503	ZIPCode_90504	ZIPCode_90505	ZIPCode_90509	ZIPCode_90601	ZIPCode_90623	ZIPCode_90630	ZIPCode_90638	ZIPCode_90639	ZIPCode_90640	ZIPCode_90650	ZIPCode_90717	ZIPCode_90720	ZIPCode_90740	ZIPCode_90745	ZIPCode_90747	ZIPCode_90755	ZIPCode_90813	ZIPCode_90840	ZIPCode_91006	ZIPCode_91007	ZIPCode_91016	ZIPCode_91024	ZIPCode_91030	ZIPCode_91040	ZIPCode_91101	ZIPCode_91103	ZIPCode_91105	ZIPCode_91107	ZIPCode_91109	ZIPCode_91116	ZIPCode_91125	ZIPCode_91129	ZIPCode_91203	ZIPCode_91207	ZIPCode_91301	ZIPCode_91302	ZIPCode_91304	ZIPCode_91311	ZIPCode_91320	ZIPCode_91326	ZIPCode_91330	ZIPCode_91335	ZIPCode_91342	ZIPCode_91343	ZIPCode_91345	ZIPCode_91355	ZIPCode_91360	ZIPCode_91361	ZIPCode_91365	ZIPCode_91367	ZIPCode_91380	ZIPCode_91401	ZIPCode_91423	ZIPCode_91604	ZIPCode_91605	ZIPCode_91614	ZIPCode_91706	ZIPCode_91709	ZIPCode_91710	ZIPCode_91711	ZIPCode_91730	ZIPCode_91741	ZIPCode_91745	ZIPCode_91754	ZIPCode_91763	ZIPCode_91765	ZIPCode_91768	ZIPCode_91770	ZIPCode_91773	ZIPCode_91775	ZIPCode_91784	ZIPCode_91791	ZIPCode_91801	ZIPCode_91902	ZIPCode_91910	ZIPCode_91911	ZIPCode_91941	ZIPCode_91942	ZIPCode_91950	ZIPCode_92007	ZIPCode_92008	ZIPCode_92009	ZIPCode_92024	ZIPCode_92028	ZIPCode_92029	ZIPCode_92037	ZIPCode_92038	ZIPCode_92054	ZIPCode_92056	ZIPCode_92064	ZIPCode_92068	ZIPCode_92069	ZIPCode_92084	ZIPCode_92093	ZIPCode_92096	ZIPCode_92101	ZIPCode_92103	ZIPCode_92104	ZIPCode_92106	ZIPCode_92109	ZIPCode_92110	ZIPCode_92115	ZIPCode_92116	ZIPCode_92120	ZIPCode_92121	ZIPCode_92122	ZIPCode_92123	ZIPCode_92124	ZIPCode_92126	ZIPCode_92129	ZIPCode_92130	ZIPCode_92131	ZIPCode_92152	ZIPCode_92154	ZIPCode_92161	ZIPCode_92173	ZIPCode_92177	ZIPCode_92182	ZIPCode_92192	ZIPCode_92220	ZIPCode_92251	ZIPCode_92325	ZIPCode_92333	ZIPCode_92346	ZIPCode_92350	ZIPCode_92354	ZIPCode_92373	ZIPCode_92374	ZIPCode_92399	ZIPCode_92407	ZIPCode_92507	ZIPCode_92518	ZIPCode_92521	ZIPCode_92606	ZIPCode_92612	ZIPCode_92614	ZIPCode_92624	ZIPCode_92626	ZIPCode_92630	ZIPCode_92634	ZIPCode_92646	ZIPCode_92647	ZIPCode_92648	ZIPCode_92653	ZIPCode_92660	ZIPCode_92661	ZIPCode_92672	ZIPCode_92673	ZIPCode_92675	ZIPCode_92677	ZIPCode_92691	ZIPCode_92692	ZIPCode_92694	ZIPCode_92697	ZIPCode_92703	ZIPCode_92704	ZIPCode_92705	ZIPCode_92709	ZIPCode_92717	ZIPCode_92735	ZIPCode_92780	ZIPCode_92806	ZIPCode_92807	ZIPCode_92821	ZIPCode_92831	ZIPCode_92833	ZIPCode_92834	ZIPCode_92835	ZIPCode_92843	ZIPCode_92866	ZIPCode_92867	ZIPCode_92868	ZIPCode_92870	ZIPCode_92886	ZIPCode_93003	ZIPCode_93009	ZIPCode_93010	ZIPCode_93014	ZIPCode_93022	ZIPCode_93023	ZIPCode_93033	ZIPCode_93063	ZIPCode_93065	ZIPCode_93077	ZIPCode_93101	ZIPCode_93105	ZIPCode_93106	ZIPCode_93107	ZIPCode_93108	ZIPCode_93109	ZIPCode_93111	ZIPCode_93117	ZIPCode_93118	ZIPCode_93302	ZIPCode_93305	ZIPCode_93311	ZIPCode_93401	ZIPCode_93403	ZIPCode_93407	ZIPCode_93437	ZIPCode_93460	ZIPCode_93524	ZIPCode_93555	ZIPCode_93561	ZIPCode_93611	ZIPCode_93657	ZIPCode_93711	ZIPCode_93720	ZIPCode_93727	ZIPCode_93907	ZIPCode_93933	ZIPCode_93940	ZIPCode_93943	ZIPCode_93950	ZIPCode_93955	ZIPCode_94002	ZIPCode_94005	ZIPCode_94010	ZIPCode_94015	ZIPCode_94019	ZIPCode_94022	ZIPCode_94024	ZIPCode_94025	ZIPCode_94028	ZIPCode_94035	ZIPCode_94040	ZIPCode_94043	ZIPCode_94061	ZIPCode_94063	ZIPCode_94065	ZIPCode_94066	ZIPCode_94080	ZIPCode_94085	ZIPCode_94086	ZIPCode_94087	ZIPCode_94102	ZIPCode_94104	ZIPCode_94105	ZIPCode_94107	ZIPCode_94108	ZIPCode_94109	ZIPCode_94110	ZIPCode_94111	ZIPCode_94112	ZIPCode_94114	ZIPCode_94115	ZIPCode_94116	ZIPCode_94117	ZIPCode_94118	ZIPCode_94122	ZIPCode_94123	ZIPCode_94124	ZIPCode_94126	ZIPCode_94131	ZIPCode_94132	ZIPCode_94143	ZIPCode_94234	ZIPCode_94301	ZIPCode_94302	ZIPCode_94303	ZIPCode_94304	ZIPCode_94305	ZIPCode_94306	ZIPCode_94309	ZIPCode_94402	ZIPCode_94404	ZIPCode_94501	ZIPCode_94507	ZIPCode_94509	ZIPCode_94521	ZIPCode_94523	ZIPCode_94526	ZIPCode_94534	ZIPCode_94536	ZIPCode_94538	ZIPCode_94539	ZIPCode_94542	ZIPCode_94545	ZIPCode_94546	ZIPCode_94550	ZIPCode_94551	ZIPCode_94553	ZIPCode_94555	ZIPCode_94558	ZIPCode_94566	ZIPCode_94571	ZIPCode_94575	ZIPCode_94577	ZIPCode_94583	ZIPCode_94588	ZIPCode_94590	ZIPCode_94591	ZIPCode_94596	ZIPCode_94598	ZIPCode_94604	ZIPCode_94606	ZIPCode_94607	ZIPCode_94608	ZIPCode_94609	ZIPCode_94610	ZIPCode_94611	ZIPCode_94612	ZIPCode_94618	ZIPCode_94701	ZIPCode_94703	ZIPCode_94704	ZIPCode_94705	ZIPCode_94706	ZIPCode_94707	ZIPCode_94708	ZIPCode_94709	ZIPCode_94710	ZIPCode_94720	ZIPCode_94801	ZIPCode_94803	ZIPCode_94806	ZIPCode_94901	ZIPCode_94904	ZIPCode_94920	ZIPCode_94923	ZIPCode_94928	ZIPCode_94939	ZIPCode_94949	ZIPCode_94960	ZIPCode_94965	ZIPCode_94970	ZIPCode_94998	ZIPCode_95003	ZIPCode_95005	ZIPCode_95006	ZIPCode_95008	ZIPCode_95010	ZIPCode_95014	ZIPCode_95020	ZIPCode_95023	ZIPCode_95032	ZIPCode_95035	ZIPCode_95037	ZIPCode_95039	ZIPCode_95045	ZIPCode_95051	ZIPCode_95053	ZIPCode_95054	ZIPCode_95060	ZIPCode_95064	ZIPCode_95070	ZIPCode_95112	ZIPCode_95120	ZIPCode_95123	ZIPCode_95125	ZIPCode_95126	ZIPCode_95131	ZIPCode_95133	ZIPCode_95134	ZIPCode_95135	ZIPCode_95136	ZIPCode_95138	ZIPCode_95192	ZIPCode_95193	ZIPCode_95207	ZIPCode_95211	ZIPCode_95307	ZIPCode_95348	ZIPCode_95351	ZIPCode_95354	ZIPCode_95370	ZIPCode_95403	ZIPCode_95405	ZIPCode_95422	ZIPCode_95449	ZIPCode_95482	ZIPCode_95503	ZIPCode_95518	ZIPCode_95521	ZIPCode_95605	ZIPCode_95616	ZIPCode_95617	ZIPCode_95621	ZIPCode_95630	ZIPCode_95670	ZIPCode_95678	ZIPCode_95741	ZIPCode_95747	ZIPCode_95758	ZIPCode_95762	ZIPCode_95812	ZIPCode_95814	ZIPCode_95816	ZIPCode_95817	ZIPCode_95818	ZIPCode_95819	ZIPCode_95820	ZIPCode_95821	ZIPCode_95822	ZIPCode_95825	ZIPCode_95827	ZIPCode_95828	ZIPCode_95831	ZIPCode_95833	ZIPCode_95841	ZIPCode_95842	ZIPCode_95929	ZIPCode_95973	ZIPCode_96001	ZIPCode_96003	ZIPCode_96008	ZIPCode_96064	ZIPCode_96091	ZIPCode_96094	ZIPCode_96145	ZIPCode_96150	ZIPCode_96651
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2	39	15	11	1	1	0	0	0	0	0	109	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
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ploancp_df.dtypes
  

Age              int64
  Experience       int64
  Income           int64
  Family           int64
  Education        int64
                   ...
  ZIPCode_96091    uint8
  ZIPCode_96094    uint8
  ZIPCode_96145    uint8
  ZIPCode_96150    uint8
  ZIPCode_96651    uint8
  Length: 479, dtype: object

Lets drop the Target Feature (Personal Loan)

X = ploancp_df.drop(["Personal_Loan"], axis=1)
  Y = ploancp_df["Personal_Loan"]

  X = pd.get_dummies(X, drop_first=True)
  

X
  

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5000 rows × 478 columns

Split Train and Test Sets 70-30

X_train, X_test, y_train, y_test = train_test_split(
      X, Y, test_size=0.30, random_state=1
  )
  

print("Shape of Training set : ", X_train.shape)
  print("Shape of test set : ", X_test.shape)
  print("Percentage of classes in training set:")
  print(y_train.value_counts(normalize=True))
  print("Percentage of classes in test set:")
  print(y_test.value_counts(normalize=True))
  

Shape of Training set :  (3500, 478)
  Shape of test set :  (1500, 478)
  Percentage of classes in training set:
  0   0.905
  1   0.095
  Name: Personal_Loan, dtype: float64
  Percentage of classes in test set:
  0   0.901
  1   0.099
  Name: Personal_Loan, dtype: float64
  

X_train
  

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y_train
  

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y_test
  

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# There are different solvers available in Sklearn logistic regression
  # The newton-cg solver is faster for high-dimensional data
  lg = LogisticRegression(solver="newton-cg", random_state=1)
  model = lg.fit(X_train, y_train)
  

# creating confusion matrix for training sets
  confusion_matrix_sklearn_with_threshold(lg, X_train, y_train)
  

From the Confusion Matrix for the Training sets we determine that the LogisticRegression model resulted in TP=290 (6.57%), TN = 3134 (89.54%), and FP=35 (1%), FN=101 (2.89%).

log_reg_model_train_perf = model_performance_classification_sklearn_with_threshold(
      lg, X_train, y_train
  )

  print("Training performance:")
  log_reg_model_train_perf
  

Training performance:
  

	Accuracy	Recall	Precision	F1
0	0.961	0.695	0.868	0.772

The model is already at 0.953 accuracy for Training.

# creating confusion matrix for testing sets
  confusion_matrix_sklearn_with_threshold(lg, X_test, y_test)
  

From the Confusion Matrix for the Testing sets we determine that the LogisticRegression model resulted in TP=84 (5.60%), TN = 1334 (88.93%), and FP=17 (1.13%), FN=65 (4.33%).

log_reg_model_test_perf = model_performance_classification_sklearn_with_threshold(
      lg, X_test, y_test
  )

  print("Test set performance:")
  log_reg_model_test_perf
  

Test set performance:
  

	Accuracy	Recall	Precision	F1
0	0.945	0.564	0.832	0.672

Accuracy for the Testing Set is at 0.949, sligtly less than for the Training set.

ROC-AUC The area under the ROC curve (AUC) is a measure of how good a model is - The higher the AUC, the better the model is, at distinguishing between classes.

1. predict_proba Predicts the probabilities for the class 0 and 1.

Input: Train or test data

Output: Returns the predicted probabilities for class 0 and 1

1. roc_curve_score Returns the auc scores

Input: 1. Training data 2. Predicted Probability

Output: AUC scores between 0 and 1

1. roc_curve Returns the fpr, tpr and threshold values which takes the original data and predicted probabilities for the class 1.

Input: 1. Training data 2. Predicted Probability

Output: False positive rate, true positive rate and threshold values

ROC-AUC on training set

# Find the roc auc score for training data
  logit_roc_auc_train = roc_auc_score(
      y_train, lg.predict_proba(X_train)[:, 1]
  )  # The indexing represents predicted probabilities for class 1
  # Find fpr, tpr and threshold values
  fpr, tpr, thresholds = roc_curve(y_train, lg.predict_proba(X_train)[:, 1])
  plt.figure(figsize=(7, 5))
  # Plot roc curve
  plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
  plt.plot([0, 1], [0, 1], "r--")
  plt.xlim([0.0, 1.0])
  plt.ylim([0.0, 1.05])
  plt.xlabel("False Positive Rate")
  plt.ylabel("True Positive Rate")
  plt.title("Receiver operating characteristic")
  plt.legend(loc="lower right")
  plt.show()
  

ROC-AUC on test set

# Find the roc auc score for testing data
  logit_roc_auc_test = roc_auc_score(
      y_test, lg.predict_proba(X_test)[:, 1]
  )  # The indexing represents predicted probabilities for class 1
  # Find fpr, tpr and threshold values
  fpr, tpr, thresholds = roc_curve(y_test, lg.predict_proba(X_test)[:, 1])
  plt.figure(figsize=(7, 5))
  # Plot roc curve
  plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_test)
  plt.plot([0, 1], [0, 1], "r--")
  plt.xlim([0.0, 1.0])
  plt.ylim([0.0, 1.05])
  plt.xlabel("False Positive Rate")
  plt.ylabel("True Positive Rate")
  plt.title("Receiver operating characteristic")
  plt.legend(loc="lower right")
  plt.show()
  

Model is giving a generalized performance.

Model Performance Improvement

Let's see if the f1 score can be improved further, by changing the model threshold using AUC-ROC Curve.

Optimal threshold using AUC-ROC curve

Optimal thresold is the value that best separated the True positive rate and False positive rate.

# Optimal threshold as per AUC-ROC curve
  # The optimal cut off would be where tpr is high and fpr is low
  # roc_curve returns the fpr, tpr and threshold values which takes the original data and predicted probabilities for the class 1.
  fpr, tpr, thresholds = roc_curve(
      y_train, lg.predict_proba(X_train)[:, 1]
  )  # The indexing represents predicted probabilities for class 1

  optimal_idx = np.argmax(
      tpr - fpr
  )  # Finds the index that contains the max difference between tpr and fpr
  optimal_threshold_auc_roc = thresholds[
      optimal_idx
  ]  # stores the optimal threshold value
  print(optimal_threshold_auc_roc)
  

0.1462260641989123
  

Checking model performance on training set

# creating confusion matrix
  confusion_matrix_sklearn_with_threshold(
      lg, X_train, y_train, threshold=optimal_threshold_auc_roc
  )
  

From the Confusion Matrix for the Training sets we determine that the LogisticRegression model resulted in TP=299 (8.54%), TN = 2957 (84.49%), and FP=299 (8.54%), FN=32 (0.91%).

# checking model performance for this model
  log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_sklearn_with_threshold(
      lg, X_train, y_train, threshold=optimal_threshold_auc_roc
  )
  print("Training performance:")
  log_reg_model_train_perf_threshold_auc_roc
  

Training performance:
  

	Accuracy	Recall	Precision	F1
0	0.930	0.903	0.585	0.710

Notice that the accuracy is 0.930 on the training set.

Checking model performance on test set

# creating confusion matrix on test set
  confusion_matrix_sklearn_with_threshold(
      lg, X_test, y_test, threshold=optimal_threshold_auc_roc
  )
  

From the Confusion Matrix for the Testing sets we determine that the LogisticRegression model resulted in TP=122 (8.13%), TN = 1352 (83.47%), and FP=99 (6.60%), FN=27 (1.80%).

# checking model performance for this model
  log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_sklearn_with_threshold(
      lg, X_test, y_test, threshold=optimal_threshold_auc_roc
  )
  print("Test set performance:")
  log_reg_model_test_perf_threshold_auc_roc
  

Test set performance:
  

	Accuracy	Recall	Precision	F1
0	0.916	0.819	0.552	0.659

The precision of the model for both training and test set has improved but the F1 score has reduced.

Let's use Precision-Recall curve and see if we can find a better threshold

The Precision-Recall curve shows the tradeoff between Precision and Recall for different thresholds. It can be used to select optimal threshold as required to improve the model improvement.

precision_recall_curve()

Returns the fpr, tpr and threshold values

Input: 1. Original data 2. Predicted Probability

Output: False positive rate, true positive rate and threshold values

# Find the predicted probabilities for class 1
  y_scores = lg.predict_proba(X_train)[:, 1]
  # Find fpr, tpr and threshold values
  prec, rec, tre = precision_recall_curve(y_train, y_scores,)


  def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
      plt.plot(thresholds, precisions[:-1], "b--", label="precision")
      plt.plot(thresholds, recalls[:-1], "g--", label="recall")
      plt.xlabel("Threshold")
      plt.legend(loc="upper left")
      plt.ylim([0, 1])


  plt.figure(figsize=(10, 7))
  # Plot recall precision curve
  plot_prec_recall_vs_tresh(prec, rec, tre)
  plt.show()
  

At the threshold of 0.36, we get balanced recall and precision.

# setting the threshold
  optimal_threshold_curve = 0.37
  

Checking model performance on training set

# creating confusion matrix
  confusion_matrix_sklearn_with_threshold(
      lg, X_train, y_train, threshold=optimal_threshold_curve
  )
  

From the Confusion Matrix for the Training sets we determine that the LogisticRegression model resulted in TP=253 (25.3%), TN = 3103 (88.66%), and FP=66 (1.89%), FN=78 (2.23%).

log_reg_model_train_perf_threshold_curve = model_performance_classification_sklearn_with_threshold(
      lg, X_train, y_train, threshold=optimal_threshold_curve
  )
  print("Training performance:")
  log_reg_model_train_perf_threshold_curve
  

Training performance:
  

	Accuracy	Recall	Precision	F1
0	0.959	0.764	0.793	0.778

Checking model performance on test set

# creating confusion matrix
  confusion_matrix_sklearn_with_threshold(
      lg, X_test, y_test, threshold=optimal_threshold_curve
  )
  

From the Confusion Matrix for the Training sets we determine that the LogisticRegression model resulted in TP=93 (6.20%), TN = 1321 (88.07%), and FP=30 (2%), FN=56 (3.73%).

log_reg_model_test_perf_threshold_curve = model_performance_classification_sklearn_with_threshold(
      lg, X_test, y_test, threshold=optimal_threshold_curve
  )
  print("Test set performance:")
  log_reg_model_test_perf_threshold_curve
  

Test set performance:
  

	Accuracy	Recall	Precision	F1
0	0.943	0.624	0.756	0.684

Model is performing well on training and test. There's not much improvement in the model performance as the default threshold is 0.50 and here we get 0.37 as the optimal threshold.

Model Performance Summary

# training performance comparison

  models_train_comp_df = pd.concat(
      [
          log_reg_model_train_perf.T,
          log_reg_model_train_perf_threshold_auc_roc.T,
          log_reg_model_train_perf_threshold_curve.T,
      ],
      axis=1,
  )
  models_train_comp_df.columns = [
      "Logistic Regression sklearn",
      "Logistic Regression-0.15 Threshold",
      "Logistic Regression-0.37 Threshold",
  ]
  print("Training performance comparison:")
  models_train_comp_df
  

Training performance comparison:
  

	Logistic Regression sklearn	Logistic Regression-0.15 Threshold	Logistic Regression-0.37 Threshold
Accuracy	0.961	0.930	0.959
Recall	0.695	0.903	0.764
Precision	0.868	0.585	0.793
F1	0.772	0.710	0.778

# testing performance comparison

  models_test_comp_df = pd.concat(
      [
          log_reg_model_test_perf.T,
          log_reg_model_test_perf_threshold_auc_roc.T,
          log_reg_model_test_perf_threshold_curve.T,
      ],
      axis=1,
  )
  models_test_comp_df.columns = [
      "Logistic Regression sklearn",
      "Logistic Regression-0.15 Threshold",
      "Logistic Regression-0.37 Threshold",
  ]
  print("Test set performance comparison:")
  models_test_comp_df
  

Test set performance comparison:
  

	Logistic Regression sklearn	Logistic Regression-0.15 Threshold	Logistic Regression-0.37 Threshold
Accuracy	0.945	0.916	0.943
Recall	0.564	0.819	0.624
Precision	0.832	0.552	0.756
F1	0.672	0.659	0.684

We can use the Logistic Regression-0.37 Threshold which yields the highest F1 score and a slightly smaller accuracy than the Logistic Regression sklearn model.

Logistic Regression-0.15 Threshold yields the largest Recall but Recall isn't a good metric for this case because the data is not balance (between Personal Loans yes and persona Loans No).

Logistic Regression-0.37 Threshold gives F1 0.68 and this is the mode we recommend if using Logistic Regression.

Decision Tree Modelling

Use 'gini' criteria to split

from sklearn.tree import DecisionTreeClassifier
  from sklearn import tree
  import sklearn.metrics as metrics
  

dTree = DecisionTreeClassifier(criterion = 'gini', random_state=1)
  dTree.fit(X_train, y_train)
  

DecisionTreeClassifier(random_state=1)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Decision Tree Performance

#scoring
  print("Accuracy on training set : ",dTree.score(X_train, y_train))
  print("Accuracy on test set : ",dTree.score(X_test, y_test))
  

Accuracy on training set :  1.0
  Accuracy on test set :  0.9746666666666667
  

Recall should be the metric of the model because Recall gives the ratio of True positives to Actual positives, which means liability customers that actually purchase a Personal Loan product.

## Function to create confusion matrix
  def make_confusion_matrix(model,y_actual,labels=[1, 0]):
      '''
      model : classifier to predict values of X
      y_actual : ground truth

      '''
      y_predict = model.predict(X_test)
      cm=metrics.confusion_matrix( y_actual, y_predict, labels=[0, 1])
      df_cm = pd.DataFrame(cm, index = [i for i in ["Actual - No","Actual - Yes"]],
                    columns = [i for i in ['Predicted - No','Predicted - Yes']])
      group_counts = ["{0:0.0f}".format(value) for value in
                  cm.flatten()]
      group_percentages = ["{0:.2%}".format(value) for value in
                           cm.flatten()/np.sum(cm)]
      labels = [f"{v1}\n{v2}" for v1, v2 in
                zip(group_counts,group_percentages)]
      labels = np.asarray(labels).reshape(2,2)
      plt.figure(figsize = (10,7))
      sns.heatmap(df_cm, annot=labels,fmt='')
      plt.ylabel('True label')
      plt.xlabel('Predicted label')
  

##  Function to calculate recall score
  def get_recall_score(model):
      '''
      model : classifier to predict values of X

      '''
      pred_train = model.predict(X_train)
      pred_test = model.predict(X_test)
      print("Recall on training set : ",metrics.recall_score(y_train,pred_train))
      print("Recall on test set : ",metrics.recall_score(y_test,pred_test))
  

Confusion Matrix

make_confusion_matrix(dTree,y_test)
  

# Recall on train and test
  get_recall_score(dTree)
  

Recall on training set :  1.0
  Recall on test set :  0.8322147651006712
  

Visualization of Decision Tree

feature_names = list(X.columns)
  print(feature_names)
  

['Age', 'Experience', 'Income', 'Family', 'Education', 'Securities_Account', 'CD_Account', 'Online', 'CreditCard', 'CCAvg_Inc%', 'year_Mortgage_Inc%', 'ZIPCode_90005', 'ZIPCode_90007', 'ZIPCode_90009', 'ZIPCode_90011', 'ZIPCode_90016', 'ZIPCode_90018', 'ZIPCode_90019', 'ZIPCode_90024', 'ZIPCode_90025', 'ZIPCode_90027', 'ZIPCode_90028', 'ZIPCode_90029', 'ZIPCode_90032', 'ZIPCode_90033', 'ZIPCode_90034', 'ZIPCode_90035', 'ZIPCode_90036', 'ZIPCode_90037', 'ZIPCode_90041', 'ZIPCode_90044', 'ZIPCode_90045', 'ZIPCode_90048', 'ZIPCode_90049', 'ZIPCode_90057', 'ZIPCode_90058', 'ZIPCode_90059', 'ZIPCode_90064', 'ZIPCode_90065', 'ZIPCode_90066', 'ZIPCode_90068', 'ZIPCode_90071', 'ZIPCode_90073', 'ZIPCode_90086', 'ZIPCode_90089', 'ZIPCode_90095', 'ZIPCode_90210', 'ZIPCode_90212', 'ZIPCode_90230', 'ZIPCode_90232', 'ZIPCode_90245', 'ZIPCode_90250', 'ZIPCode_90254', 'ZIPCode_90266', 'ZIPCode_90272', 'ZIPCode_90274', 'ZIPCode_90275', 'ZIPCode_90277', 'ZIPCode_90280', 'ZIPCode_90291', 'ZIPCode_90304', 'ZIPCode_90401', 'ZIPCode_90404', 'ZIPCode_90405', 'ZIPCode_90502', 'ZIPCode_90503', 'ZIPCode_90504', 'ZIPCode_90505', 'ZIPCode_90509', 'ZIPCode_90601', 'ZIPCode_90623', 'ZIPCode_90630', 'ZIPCode_90638', 'ZIPCode_90639', 'ZIPCode_90640', 'ZIPCode_90650', 'ZIPCode_90717', 'ZIPCode_90720', 'ZIPCode_90740', 'ZIPCode_90745', 'ZIPCode_90747', 'ZIPCode_90755', 'ZIPCode_90813', 'ZIPCode_90840', 'ZIPCode_91006', 'ZIPCode_91007', 'ZIPCode_91016', 'ZIPCode_91024', 'ZIPCode_91030', 'ZIPCode_91040', 'ZIPCode_91101', 'ZIPCode_91103', 'ZIPCode_91105', 'ZIPCode_91107', 'ZIPCode_91109', 'ZIPCode_91116', 'ZIPCode_91125', 'ZIPCode_91129', 'ZIPCode_91203', 'ZIPCode_91207', 'ZIPCode_91301', 'ZIPCode_91302', 'ZIPCode_91304', 'ZIPCode_91311', 'ZIPCode_91320', 'ZIPCode_91326', 'ZIPCode_91330', 'ZIPCode_91335', 'ZIPCode_91342', 'ZIPCode_91343', 'ZIPCode_91345', 'ZIPCode_91355', 'ZIPCode_91360', 'ZIPCode_91361', 'ZIPCode_91365', 'ZIPCode_91367', 'ZIPCode_91380', 'ZIPCode_91401', 'ZIPCode_91423', 'ZIPCode_91604', 'ZIPCode_91605', 'ZIPCode_91614', 'ZIPCode_91706', 'ZIPCode_91709', 'ZIPCode_91710', 'ZIPCode_91711', 'ZIPCode_91730', 'ZIPCode_91741', 'ZIPCode_91745', 'ZIPCode_91754', 'ZIPCode_91763', 'ZIPCode_91765', 'ZIPCode_91768', 'ZIPCode_91770', 'ZIPCode_91773', 'ZIPCode_91775', 'ZIPCode_91784', 'ZIPCode_91791', 'ZIPCode_91801', 'ZIPCode_91902', 'ZIPCode_91910', 'ZIPCode_91911', 'ZIPCode_91941', 'ZIPCode_91942', 'ZIPCode_91950', 'ZIPCode_92007', 'ZIPCode_92008', 'ZIPCode_92009', 'ZIPCode_92024', 'ZIPCode_92028', 'ZIPCode_92029', 'ZIPCode_92037', 'ZIPCode_92038', 'ZIPCode_92054', 'ZIPCode_92056', 'ZIPCode_92064', 'ZIPCode_92068', 'ZIPCode_92069', 'ZIPCode_92084', 'ZIPCode_92093', 'ZIPCode_92096', 'ZIPCode_92101', 'ZIPCode_92103', 'ZIPCode_92104', 'ZIPCode_92106', 'ZIPCode_92109', 'ZIPCode_92110', 'ZIPCode_92115', 'ZIPCode_92116', 'ZIPCode_92120', 'ZIPCode_92121', 'ZIPCode_92122', 'ZIPCode_92123', 'ZIPCode_92124', 'ZIPCode_92126', 'ZIPCode_92129', 'ZIPCode_92130', 'ZIPCode_92131', 'ZIPCode_92152', 'ZIPCode_92154', 'ZIPCode_92161', 'ZIPCode_92173', 'ZIPCode_92177', 'ZIPCode_92182', 'ZIPCode_92192', 'ZIPCode_92220', 'ZIPCode_92251', 'ZIPCode_92325', 'ZIPCode_92333', 'ZIPCode_92346', 'ZIPCode_92350', 'ZIPCode_92354', 'ZIPCode_92373', 'ZIPCode_92374', 'ZIPCode_92399', 'ZIPCode_92407', 'ZIPCode_92507', 'ZIPCode_92518', 'ZIPCode_92521', 'ZIPCode_92606', 'ZIPCode_92612', 'ZIPCode_92614', 'ZIPCode_92624', 'ZIPCode_92626', 'ZIPCode_92630', 'ZIPCode_92634', 'ZIPCode_92646', 'ZIPCode_92647', 'ZIPCode_92648', 'ZIPCode_92653', 'ZIPCode_92660', 'ZIPCode_92661', 'ZIPCode_92672', 'ZIPCode_92673', 'ZIPCode_92675', 'ZIPCode_92677', 'ZIPCode_92691', 'ZIPCode_92692', 'ZIPCode_92694', 'ZIPCode_92697', 'ZIPCode_92703', 'ZIPCode_92704', 'ZIPCode_92705', 'ZIPCode_92709', 'ZIPCode_92717', 'ZIPCode_92735', 'ZIPCode_92780', 'ZIPCode_92806', 'ZIPCode_92807', 'ZIPCode_92821', 'ZIPCode_92831', 'ZIPCode_92833', 'ZIPCode_92834', 'ZIPCode_92835', 'ZIPCode_92843', 'ZIPCode_92866', 'ZIPCode_92867', 'ZIPCode_92868', 'ZIPCode_92870', 'ZIPCode_92886', 'ZIPCode_93003', 'ZIPCode_93009', 'ZIPCode_93010', 'ZIPCode_93014', 'ZIPCode_93022', 'ZIPCode_93023', 'ZIPCode_93033', 'ZIPCode_93063', 'ZIPCode_93065', 'ZIPCode_93077', 'ZIPCode_93101', 'ZIPCode_93105', 'ZIPCode_93106', 'ZIPCode_93107', 'ZIPCode_93108', 'ZIPCode_93109', 'ZIPCode_93111', 'ZIPCode_93117', 'ZIPCode_93118', 'ZIPCode_93302', 'ZIPCode_93305', 'ZIPCode_93311', 'ZIPCode_93401', 'ZIPCode_93403', 'ZIPCode_93407', 'ZIPCode_93437', 'ZIPCode_93460', 'ZIPCode_93524', 'ZIPCode_93555', 'ZIPCode_93561', 'ZIPCode_93611', 'ZIPCode_93657', 'ZIPCode_93711', 'ZIPCode_93720', 'ZIPCode_93727', 'ZIPCode_93907', 'ZIPCode_93933', 'ZIPCode_93940', 'ZIPCode_93943', 'ZIPCode_93950', 'ZIPCode_93955', 'ZIPCode_94002', 'ZIPCode_94005', 'ZIPCode_94010', 'ZIPCode_94015', 'ZIPCode_94019', 'ZIPCode_94022', 'ZIPCode_94024', 'ZIPCode_94025', 'ZIPCode_94028', 'ZIPCode_94035', 'ZIPCode_94040', 'ZIPCode_94043', 'ZIPCode_94061', 'ZIPCode_94063', 'ZIPCode_94065', 'ZIPCode_94066', 'ZIPCode_94080', 'ZIPCode_94085', 'ZIPCode_94086', 'ZIPCode_94087', 'ZIPCode_94102', 'ZIPCode_94104', 'ZIPCode_94105', 'ZIPCode_94107', 'ZIPCode_94108', 'ZIPCode_94109', 'ZIPCode_94110', 'ZIPCode_94111', 'ZIPCode_94112', 'ZIPCode_94114', 'ZIPCode_94115', 'ZIPCode_94116', 'ZIPCode_94117', 'ZIPCode_94118', 'ZIPCode_94122', 'ZIPCode_94123', 'ZIPCode_94124', 'ZIPCode_94126', 'ZIPCode_94131', 'ZIPCode_94132', 'ZIPCode_94143', 'ZIPCode_94234', 'ZIPCode_94301', 'ZIPCode_94302', 'ZIPCode_94303', 'ZIPCode_94304', 'ZIPCode_94305', 'ZIPCode_94306', 'ZIPCode_94309', 'ZIPCode_94402', 'ZIPCode_94404', 'ZIPCode_94501', 'ZIPCode_94507', 'ZIPCode_94509', 'ZIPCode_94521', 'ZIPCode_94523', 'ZIPCode_94526', 'ZIPCode_94534', 'ZIPCode_94536', 'ZIPCode_94538', 'ZIPCode_94539', 'ZIPCode_94542', 'ZIPCode_94545', 'ZIPCode_94546', 'ZIPCode_94550', 'ZIPCode_94551', 'ZIPCode_94553', 'ZIPCode_94555', 'ZIPCode_94558', 'ZIPCode_94566', 'ZIPCode_94571', 'ZIPCode_94575', 'ZIPCode_94577', 'ZIPCode_94583', 'ZIPCode_94588', 'ZIPCode_94590', 'ZIPCode_94591', 'ZIPCode_94596', 'ZIPCode_94598', 'ZIPCode_94604', 'ZIPCode_94606', 'ZIPCode_94607', 'ZIPCode_94608', 'ZIPCode_94609', 'ZIPCode_94610', 'ZIPCode_94611', 'ZIPCode_94612', 'ZIPCode_94618', 'ZIPCode_94701', 'ZIPCode_94703', 'ZIPCode_94704', 'ZIPCode_94705', 'ZIPCode_94706', 'ZIPCode_94707', 'ZIPCode_94708', 'ZIPCode_94709', 'ZIPCode_94710', 'ZIPCode_94720', 'ZIPCode_94801', 'ZIPCode_94803', 'ZIPCode_94806', 'ZIPCode_94901', 'ZIPCode_94904', 'ZIPCode_94920', 'ZIPCode_94923', 'ZIPCode_94928', 'ZIPCode_94939', 'ZIPCode_94949', 'ZIPCode_94960', 'ZIPCode_94965', 'ZIPCode_94970', 'ZIPCode_94998', 'ZIPCode_95003', 'ZIPCode_95005', 'ZIPCode_95006', 'ZIPCode_95008', 'ZIPCode_95010', 'ZIPCode_95014', 'ZIPCode_95020', 'ZIPCode_95023', 'ZIPCode_95032', 'ZIPCode_95035', 'ZIPCode_95037', 'ZIPCode_95039', 'ZIPCode_95045', 'ZIPCode_95051', 'ZIPCode_95053', 'ZIPCode_95054', 'ZIPCode_95060', 'ZIPCode_95064', 'ZIPCode_95070', 'ZIPCode_95112', 'ZIPCode_95120', 'ZIPCode_95123', 'ZIPCode_95125', 'ZIPCode_95126', 'ZIPCode_95131', 'ZIPCode_95133', 'ZIPCode_95134', 'ZIPCode_95135', 'ZIPCode_95136', 'ZIPCode_95138', 'ZIPCode_95192', 'ZIPCode_95193', 'ZIPCode_95207', 'ZIPCode_95211', 'ZIPCode_95307', 'ZIPCode_95348', 'ZIPCode_95351', 'ZIPCode_95354', 'ZIPCode_95370', 'ZIPCode_95403', 'ZIPCode_95405', 'ZIPCode_95422', 'ZIPCode_95449', 'ZIPCode_95482', 'ZIPCode_95503', 'ZIPCode_95518', 'ZIPCode_95521', 'ZIPCode_95605', 'ZIPCode_95616', 'ZIPCode_95617', 'ZIPCode_95621', 'ZIPCode_95630', 'ZIPCode_95670', 'ZIPCode_95678', 'ZIPCode_95741', 'ZIPCode_95747', 'ZIPCode_95758', 'ZIPCode_95762', 'ZIPCode_95812', 'ZIPCode_95814', 'ZIPCode_95816', 'ZIPCode_95817', 'ZIPCode_95818', 'ZIPCode_95819', 'ZIPCode_95820', 'ZIPCode_95821', 'ZIPCode_95822', 'ZIPCode_95825', 'ZIPCode_95827', 'ZIPCode_95828', 'ZIPCode_95831', 'ZIPCode_95833', 'ZIPCode_95841', 'ZIPCode_95842', 'ZIPCode_95929', 'ZIPCode_95973', 'ZIPCode_96001', 'ZIPCode_96003', 'ZIPCode_96008', 'ZIPCode_96064', 'ZIPCode_96091', 'ZIPCode_96094', 'ZIPCode_96145', 'ZIPCode_96150', 'ZIPCode_96651']
  

plt.figure(figsize=(20,30))
  tree.plot_tree(dTree,feature_names=feature_names,filled=True,fontsize=9,node_ids=True,class_names=True)
  plt.show()
  

# Text report showing the rules of a decision tree -

  print(tree.export_text(dTree,feature_names=feature_names,show_weights=True))
  

|--- Income <= 116.50
  |   |--- Income <= 92.50
  |   |   |--- ZIPCode_90601 <= 0.50
  |   |   |   |--- Income <= 81.50
  |   |   |   |   |--- ZIPCode_91203 <= 0.50
  |   |   |   |   |   |--- ZIPCode_94305 <= 0.50
  |   |   |   |   |   |   |--- weights: [2198.00, 0.00] class: 0
  |   |   |   |   |   |--- ZIPCode_94305 >  0.50
  |   |   |   |   |   |   |--- CCAvg_Inc% <= 52.50
  |   |   |   |   |   |   |   |--- weights: [45.00, 0.00] class: 0
  |   |   |   |   |   |   |--- CCAvg_Inc% >  52.50
  |   |   |   |   |   |   |   |--- CCAvg_Inc% <= 53.50
  |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |   |   |--- CCAvg_Inc% >  53.50
  |   |   |   |   |   |   |   |   |--- Income <= 71.00
  |   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0
  |   |   |   |   |   |   |   |   |--- Income >  71.00
  |   |   |   |   |   |   |   |   |   |--- Age <= 43.50
  |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |   |   |   |   |--- Age >  43.50
  |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
  |   |   |   |   |--- ZIPCode_91203 >  0.50
  |   |   |   |   |   |--- CreditCard <= 0.50
  |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |--- CreditCard >  0.50
  |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
  |   |   |   |--- Income >  81.50
  |   |   |   |   |--- CCAvg_Inc% <= 43.50
  |   |   |   |   |   |--- ZIPCode_92121 <= 0.50
  |   |   |   |   |   |   |--- weights: [248.00, 0.00] class: 0
  |   |   |   |   |   |--- ZIPCode_92121 >  0.50
  |   |   |   |   |   |   |--- CreditCard <= 0.50
  |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
  |   |   |   |   |   |   |--- CreditCard >  0.50
  |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |--- CCAvg_Inc% >  43.50
  |   |   |   |   |   |--- CD_Account <= 0.50
  |   |   |   |   |   |   |--- Income <= 83.50
  |   |   |   |   |   |   |   |--- Age <= 45.50
  |   |   |   |   |   |   |   |   |--- Age <= 28.00
  |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |   |   |   |--- Age >  28.00
  |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
  |   |   |   |   |   |   |   |--- Age >  45.50
  |   |   |   |   |   |   |   |   |--- year_Mortgage_Inc% <= 5.00
  |   |   |   |   |   |   |   |   |   |--- ZIPCode_94720 <= 0.50
  |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
  |   |   |   |   |   |   |   |   |   |--- ZIPCode_94720 >  0.50
  |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
  |   |   |   |   |   |   |   |   |--- year_Mortgage_Inc% >  5.00
  |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
  |   |   |   |   |   |   |--- Income >  83.50
  |   |   |   |   |   |   |   |--- ZIPCode_94122 <= 0.50
  |   |   |   |   |   |   |   |   |--- weights: [30.00, 0.00] class: 0
  |   |   |   |   |   |   |   |--- ZIPCode_94122 >  0.50
  |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |--- CD_Account >  0.50
  |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
  |   |   |--- ZIPCode_90601 >  0.50
  |   |   |   |--- Income <= 71.00
  |   |   |   |   |--- weights: [2.00, 0.00] class: 0
  |   |   |   |--- Income >  71.00
  |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |--- Income >  92.50
  |   |   |--- CCAvg_Inc% <= 37.50
  |   |   |   |--- ZIPCode_93106 <= 0.50
  |   |   |   |   |--- ZIPCode_90049 <= 0.50
  |   |   |   |   |   |--- ZIPCode_95822 <= 0.50
  |   |   |   |   |   |   |--- ZIPCode_94705 <= 0.50
  |   |   |   |   |   |   |   |--- ZIPCode_91129 <= 0.50
  |   |   |   |   |   |   |   |   |--- ZIPCode_92007 <= 0.50
  |   |   |   |   |   |   |   |   |   |--- ZIPCode_94110 <= 0.50
  |   |   |   |   |   |   |   |   |   |   |--- ZIPCode_94305 <= 0.50
  |   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
  |   |   |   |   |   |   |   |   |   |   |--- ZIPCode_94305 >  0.50
  |   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
  |   |   |   |   |   |   |   |   |   |--- ZIPCode_94110 >  0.50
  |   |   |   |   |   |   |   |   |   |   |--- Age <= 48.50
  |   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
  |   |   |   |   |   |   |   |   |   |   |--- Age >  48.50
  |   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |   |   |   |--- ZIPCode_92007 >  0.50
  |   |   |   |   |   |   |   |   |   |--- Education <= 2.00
  |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
  |   |   |   |   |   |   |   |   |   |--- Education >  2.00
  |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |   |   |--- ZIPCode_91129 >  0.50
  |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |   |--- ZIPCode_94705 >  0.50
  |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |--- ZIPCode_95822 >  0.50
  |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |--- ZIPCode_90049 >  0.50
  |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |--- ZIPCode_93106 >  0.50
  |   |   |   |   |--- Experience <= 33.50
  |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
  |   |   |   |   |--- Experience >  33.50
  |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
  |   |   |--- CCAvg_Inc% >  37.50
  |   |   |   |--- Education <= 1.50
  |   |   |   |   |--- CD_Account <= 0.50
  |   |   |   |   |   |--- Experience <= 5.00
  |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |--- Experience >  5.00
  |   |   |   |   |   |   |--- ZIPCode_92709 <= 0.50
  |   |   |   |   |   |   |   |--- ZIPCode_90034 <= 0.50
  |   |   |   |   |   |   |   |   |--- weights: [24.00, 0.00] class: 0
  |   |   |   |   |   |   |   |--- ZIPCode_90034 >  0.50
  |   |   |   |   |   |   |   |   |--- Age <= 50.00
  |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
  |   |   |   |   |   |   |   |   |--- Age >  50.00
  |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |   |--- ZIPCode_92709 >  0.50
  |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |--- CD_Account >  0.50
  |   |   |   |   |   |--- CCAvg_Inc% <= 59.00
  |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
  |   |   |   |   |   |--- CCAvg_Inc% >  59.00
  |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
  |   |   |   |--- Education >  1.50
  |   |   |   |   |--- Income <= 106.00
  |   |   |   |   |   |--- Family <= 2.50
  |   |   |   |   |   |   |--- CCAvg_Inc% <= 51.50
  |   |   |   |   |   |   |   |--- CD_Account <= 0.50
  |   |   |   |   |   |   |   |   |--- ZIPCode_90277 <= 0.50
  |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
  |   |   |   |   |   |   |   |   |--- ZIPCode_90277 >  0.50
  |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
  |   |   |   |   |   |   |   |--- CD_Account >  0.50
  |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
  |   |   |   |   |   |   |--- CCAvg_Inc% >  51.50
  |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
  |   |   |   |   |   |--- Family >  2.50
  |   |   |   |   |   |   |--- Age <= 62.50
  |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1
  |   |   |   |   |   |   |--- Age >  62.50
  |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
  |   |   |   |   |--- Income >  106.00
  |   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
  |--- Income >  116.50
  |   |--- Education <= 1.50
  |   |   |--- Family <= 2.50
  |   |   |   |--- weights: [375.00, 0.00] class: 0
  |   |   |--- Family >  2.50
  |   |   |   |--- weights: [0.00, 47.00] class: 1
  |   |--- Education >  1.50
  |   |   |--- weights: [0.00, 222.00] class: 1

  

# importance of features in the tree building ( The importance of a feature is computed as the
  #(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

  print (pd.DataFrame(dTree.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))
  

                Imp
  Education     0.403
  Income        0.310
  Family        0.143
  CCAvg_Inc%    0.049
  Age           0.020
  ...             ...
  ZIPCode_92110 0.000
  ZIPCode_92109 0.000
  ZIPCode_92106 0.000
  ZIPCode_92104 0.000
  ZIPCode_96651 0.000

  [478 rows x 1 columns]
  

According to the decision tree model, Education is the most important variable for predicting if the liaility customer will purchase a Personal Loan product. Notice that from our previous analysis, we determined that Income was the most important feature.

The tree above is very complex, such a tree often overfits. So lets reduce dimensions.

Reducing Over Fitting

dTree1 = DecisionTreeClassifier(criterion = 'gini',max_depth=3,random_state=1)
  dTree1.fit(X_train, y_train)
  

DecisionTreeClassifier(max_depth=3, random_state=1)

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Confusion Matrix - decision tree with depth restricted to 3

make_confusion_matrix(dTree1, y_test)
  

# Accuracy on train and test
  print("Accuracy on training set : ",dTree1.score(X_train, y_train))
  print("Accuracy on test set : ",dTree1.score(X_test, y_test))
  # Recall on train and test
  get_recall_score(dTree1)
  

Accuracy on training set :  0.9822857142857143
  Accuracy on test set :  0.9753333333333334
  Recall on training set :  0.8126888217522659
  Recall on test set :  0.7516778523489933
  

Recall on Training has reduced from 1 to 0.81 which is better because the model overfitting has been reduced.

Visualization of Decision Tree

plt.figure(figsize=(15,10))

  tree.plot_tree(dTree1,feature_names=feature_names,filled=True,fontsize=9,node_ids=True,class_names=True)
  plt.show()
  

# Text report showing the rules of a decision tree -

  print(tree.export_text(dTree1,feature_names=feature_names,show_weights=True))
  

|--- Income <= 116.50
  |   |--- Income <= 92.50
  |   |   |--- ZIPCode_90601 <= 0.50
  |   |   |   |--- weights: [2550.00, 14.00] class: 0
  |   |   |--- ZIPCode_90601 >  0.50
  |   |   |   |--- weights: [2.00, 1.00] class: 0
  |   |--- Income >  92.50
  |   |   |--- CCAvg_Inc% <= 37.50
  |   |   |   |--- weights: [207.00, 14.00] class: 0
  |   |   |--- CCAvg_Inc% >  37.50
  |   |   |   |--- weights: [35.00, 33.00] class: 0
  |--- Income >  116.50
  |   |--- Education <= 1.50
  |   |   |--- Family <= 2.50
  |   |   |   |--- weights: [375.00, 0.00] class: 0
  |   |   |--- Family >  2.50
  |   |   |   |--- weights: [0.00, 47.00] class: 1
  |   |--- Education >  1.50
  |   |   |--- weights: [0.00, 222.00] class: 1

  

The tree has become readable and the recall on the test set has reduced from 0.83 to 0.75

# importance of features in the tree building ( The importance of a feature is computed as the
  #(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

  print (pd.DataFrame(dTree1.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))
  

                Imp
  Education     0.450
  Income        0.348
  Family        0.164
  CCAvg_Inc%    0.036
  ZIPCode_90601 0.001
  ...             ...
  ZIPCode_92093 0.000
  ZIPCode_92084 0.000
  ZIPCode_92069 0.000
  ZIPCode_92068 0.000
  ZIPCode_96651 0.000

  [478 rows x 1 columns]
  

With a depth of 3, the model is undefitting. So we are going to find the best values.

Using GridSearch for Hyperparameter tuning of our tree model

Notes: Hyperparameter tuning is also tricky in the sense that there is no direct way to calculate how a change in the hyperparameter value will reduce the loss of your model, so we usually resort to experimentation. i.e we'll use Grid search Grid search is a tuning technique that attempts to compute the optimum values of hyperparameters. It is an exhaustive search that is performed on a the specific parameter values of a model. The parameters of the estimator/model used to apply these methods are optimized by cross-validated grid-searc

from sklearn.model_selection import GridSearchCV
  

# Choose the type of classifier.
  estimator = DecisionTreeClassifier(random_state=1)

  # Grid of parameters to choose from
  ## add from article
  parameters = {'max_depth': np.arange(1,10),
                'min_samples_leaf': [1, 2, 5, 7, 10,15,20],
                'max_leaf_nodes' : [2, 3, 5, 10],
                'min_impurity_decrease': [0.001,0.01,0.1]
               }

  # Type of scoring used to compare parameter combinations
  acc_scorer = metrics.make_scorer(metrics.recall_score)

  # Run the grid search
  grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer,cv=5)
  grid_obj = grid_obj.fit(X_train, y_train)

  # Set the clf to the best combination of parameters
  estimator = grid_obj.best_estimator_

  # Fit the best algorithm to the data.
  estimator.fit(X_train, y_train)
  

DecisionTreeClassifier(max_depth=4, max_leaf_nodes=10,
                         min_impurity_decrease=0.001, min_samples_leaf=7,
                         random_state=1)

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Confusion Matrix - decision tree with tuned hyperparameters

make_confusion_matrix(estimator,y_test)
  

# Accuracy on train and test
  print("Accuracy on training set : ",estimator.score(X_train, y_train))
  print("Accuracy on test set : ",estimator.score(X_test, y_test))
  # Recall on train and test
  get_recall_score(estimator)
  

Accuracy on training set :  0.9871428571428571
  Accuracy on test set :  0.9786666666666667
  Recall on training set :  0.8912386706948641
  Recall on test set :  0.8187919463087249
  

Recall is worse for both train and test set after hyperparameter tuning.

Decision Tree Visualization

plt.figure(figsize=(15,10))

  tree.plot_tree(estimator,feature_names=feature_names,filled=True,fontsize=9,node_ids=True,class_names=True)
  plt.show()
  

# Text report showing the rules of a decision tree -

  print(tree.export_text(estimator,feature_names=feature_names,show_weights=True))
  

|--- Income <= 116.50
  |   |--- Income <= 92.50
  |   |   |--- weights: [2552.00, 15.00] class: 0
  |   |--- Income >  92.50
  |   |   |--- CCAvg_Inc% <= 37.50
  |   |   |   |--- weights: [207.00, 14.00] class: 0
  |   |   |--- CCAvg_Inc% >  37.50
  |   |   |   |--- Education <= 1.50
  |   |   |   |   |--- weights: [26.00, 7.00] class: 0
  |   |   |   |--- Education >  1.50
  |   |   |   |   |--- weights: [9.00, 26.00] class: 1
  |--- Income >  116.50
  |   |--- Education <= 1.50
  |   |   |--- Family <= 2.50
  |   |   |   |--- weights: [375.00, 0.00] class: 0
  |   |   |--- Family >  2.50
  |   |   |   |--- weights: [0.00, 47.00] class: 1
  |   |--- Education >  1.50
  |   |   |--- weights: [0.00, 222.00] class: 1

  

# importance of features in the tree building ( The importance of a feature is computed as the
  #(normalized) total reduction of the 'criterion' brought by that feature. It is also known as the Gini importance )

  print (pd.DataFrame(estimator.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

  #Here we will see that importance of features has increased
  

                Imp
  Education     0.461
  Income        0.342
  Family        0.161
  CCAvg_Inc%    0.036
  ZIPCode_94234 0.000
  ...             ...
  ZIPCode_92084 0.000
  ZIPCode_92069 0.000
  ZIPCode_92068 0.000
  ZIPCode_92064 0.000
  ZIPCode_96651 0.000

  [478 rows x 1 columns]
  

Post pruning might give better results, since there is quite a good possibility that we might neglect some hyperparameters, post pruning will take care of all that.

Cost Complexity Pruning

Notes: The DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to prevent a tree from overfiting. Cost complexity pruning provides another option to control the size of a tree. In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. Greater values of ccp_alpha increase the number of nodes pruned. Here we only show the effect of ccp_alpha on regularizing the trees and how to choose a ccp_alpha based on validation scores.

Total impurity of leaves vs effective alphas of pruned tree

Notes: Minimal cost complexity pruning recursively finds the node with the "weakest link". The weakest link is characterized by an effective alpha, where the nodes with the smallest effective alpha are pruned first. To get an idea of what values of ccp_alpha could be appropriate, scikit-learn provides DecisionTreeClassifier.cost_complexity_pruning_path that returns the effective alphas and the corresponding total leaf impurities at each step of the pruning process. As alpha increases, more of the tree is pruned, which increases the total impurity of its leaves.

clf = DecisionTreeClassifier(random_state=1)
  path = clf.cost_complexity_pruning_path(X_train, y_train)
  ccp_alphas, impurities = path.ccp_alphas, path.impurities
  

pd.DataFrame(path)
  

	ccp_alphas	impurities
0	0.000	0.000
1	0.000	0.001
2	0.000	0.002
3	0.000	0.004
4	0.000	0.005
5	0.000	0.005
6	0.000	0.005
7	0.000	0.006
8	0.000	0.007
9	0.000	0.008
10	0.000	0.008
11	0.000	0.008
12	0.000	0.009
13	0.000	0.009
14	0.000	0.010
15	0.001	0.011
16	0.001	0.013
17	0.001	0.014
18	0.001	0.014
19	0.001	0.015
20	0.001	0.019
21	0.001	0.020
22	0.001	0.022
23	0.001	0.023
24	0.003	0.026
25	0.004	0.035
26	0.024	0.059
27	0.056	0.171

fig, ax = plt.subplots(figsize=(10,5))
  ax.plot(ccp_alphas[:-1], impurities[:-1], marker='o', drawstyle="steps-post")
  ax.set_xlabel("effective alpha")
  ax.set_ylabel("total impurity of leaves")
  ax.set_title("Total Impurity vs effective alpha for training set")
  plt.show()
  

Next, we train a decision tree using the effective alphas. The last value in ccp_alphas is the alpha value that prunes the whole tree, leaving the tree, clfs[-1], with one node.

clfs = []
  for ccp_alpha in ccp_alphas:
      clf = DecisionTreeClassifier(random_state=1, ccp_alpha=ccp_alpha)
      clf.fit(X_train, y_train)
      clfs.append(clf)
  print("Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
        clfs[-1].tree_.node_count, ccp_alphas[-1]))
  

Number of nodes in the last tree is: 1 with ccp_alpha: 0.056364969335601575
  

For the remainder, we remove the last element in clfs and ccp_alphas, because it is the trivial tree with only one node. Here we show that the number of nodes and tree depth decreases as alpha increases.

clfs = clfs[:-1]
  ccp_alphas = ccp_alphas[:-1]

  node_counts = [clf.tree_.node_count for clf in clfs]
  depth = [clf.tree_.max_depth for clf in clfs]
  fig, ax = plt.subplots(2, 1,figsize=(10,7))
  ax[0].plot(ccp_alphas, node_counts, marker='o', drawstyle="steps-post")
  ax[0].set_xlabel("alpha")
  ax[0].set_ylabel("number of nodes")
  ax[0].set_title("Number of nodes vs alpha")
  ax[1].plot(ccp_alphas, depth, marker='o', drawstyle="steps-post")
  ax[1].set_xlabel("alpha")
  ax[1].set_ylabel("depth of tree")
  ax[1].set_title("Depth vs alpha")
  fig.tight_layout()
  

Accuracy vs alpha for training and testing sets

When ccp_alpha is set to zero and keeping the other default parameters of DecisionTreeClassifier, the tree overfits, leading to a 100% training accuracy and 69% testing accuracy. As alpha increases, more of the tree is pruned, thus creating a decision tree that generalizes better.

train_scores = [clf.score(X_train, y_train) for clf in clfs]
  test_scores = [clf.score(X_test, y_test) for clf in clfs]
  

fig, ax = plt.subplots(figsize=(10,5))
  ax.set_xlabel("alpha")
  ax.set_ylabel("accuracy")
  ax.set_title("Accuracy vs alpha for training and testing sets")
  ax.plot(ccp_alphas, train_scores, marker='o', label="train",
          drawstyle="steps-post")
  ax.plot(ccp_alphas, test_scores, marker='o', label="test",
          drawstyle="steps-post")
  ax.legend()
  plt.show()
  

index_best_model = np.argmax(test_scores)
  best_model = clfs[index_best_model]
  print(best_model)
  print('Training accuracy of best model: ',best_model.score(X_train, y_train))
  print('Test accuracy of best model: ',best_model.score(X_test, y_test))
  

DecisionTreeClassifier(ccp_alpha=0.0005952380952380953, random_state=1)
  Training accuracy of best model:  0.9908571428571429
  Test accuracy of best model:  0.9786666666666667
  

Since accuracy isn't the right metric for our data we would want high recall

recall_train=[]
  for clf in clfs:
      pred_train3=clf.predict(X_train)
      values_train=metrics.recall_score(y_train,pred_train3)
      recall_train.append(values_train)
  

recall_test=[]
  for clf in clfs:
      pred_test3=clf.predict(X_test)
      values_test=metrics.recall_score(y_test,pred_test3)
      recall_test.append(values_test)
  

fig, ax = plt.subplots(figsize=(15,5))
  ax.set_xlabel("alpha")
  ax.set_ylabel("Recall")
  ax.set_title("Recall vs alpha for training and testing sets")
  ax.plot(ccp_alphas, recall_train, marker='o', label="train",
          drawstyle="steps-post")
  ax.plot(ccp_alphas, recall_test, marker='o', label="test",
          drawstyle="steps-post")
  ax.legend()
  plt.show()
  

# creating the model where we get highest train and test recall
  index_best_model = np.argmax(recall_test)
  best_model = clfs[index_best_model]
  print(best_model)
  

DecisionTreeClassifier(ccp_alpha=0.0004761904761904762, random_state=1)
  

Confusion Matrix - post-pruned decision tree

make_confusion_matrix(best_model,y_test)
  

# Recall on train and test
  get_recall_score(best_model)
  

Recall on training set :  0.9577039274924471
  Recall on test set :  0.8389261744966443
  

plt.figure(figsize=(17,15))

  tree.plot_tree(best_model,feature_names=feature_names,filled=True,fontsize=9,node_ids=True,class_names=True)
  plt.show()
  

# Text report showing the rules of a decision tree -

  print(tree.export_text(best_model,feature_names=feature_names,show_weights=True))
  

|--- Income <= 116.50
  |   |--- Income <= 92.50
  |   |   |--- ZIPCode_90601 <= 0.50
  |   |   |   |--- Income <= 81.50
  |   |   |   |   |--- weights: [2258.00, 3.00] class: 0
  |   |   |   |--- Income >  81.50
  |   |   |   |   |--- CCAvg_Inc% <= 43.50
  |   |   |   |   |   |--- weights: [250.00, 1.00] class: 0
  |   |   |   |   |--- CCAvg_Inc% >  43.50
  |   |   |   |   |   |--- CD_Account <= 0.50
  |   |   |   |   |   |   |--- Income <= 83.50
  |   |   |   |   |   |   |   |--- Age <= 45.50
  |   |   |   |   |   |   |   |   |--- Age <= 28.00
  |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |   |   |   |--- Age >  28.00
  |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
  |   |   |   |   |   |   |   |--- Age >  45.50
  |   |   |   |   |   |   |   |   |--- year_Mortgage_Inc% <= 5.00
  |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 5.00] class: 1
  |   |   |   |   |   |   |   |   |--- year_Mortgage_Inc% >  5.00
  |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
  |   |   |   |   |   |   |--- Income >  83.50
  |   |   |   |   |   |   |   |--- ZIPCode_94122 <= 0.50
  |   |   |   |   |   |   |   |   |--- weights: [30.00, 0.00] class: 0
  |   |   |   |   |   |   |   |--- ZIPCode_94122 >  0.50
  |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |--- CD_Account >  0.50
  |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
  |   |   |--- ZIPCode_90601 >  0.50
  |   |   |   |--- weights: [2.00, 1.00] class: 0
  |   |--- Income >  92.50
  |   |   |--- CCAvg_Inc% <= 37.50
  |   |   |   |--- ZIPCode_93106 <= 0.50
  |   |   |   |   |--- ZIPCode_90049 <= 0.50
  |   |   |   |   |   |--- ZIPCode_95822 <= 0.50
  |   |   |   |   |   |   |--- ZIPCode_94705 <= 0.50
  |   |   |   |   |   |   |   |--- ZIPCode_91129 <= 0.50
  |   |   |   |   |   |   |   |   |--- weights: [206.00, 8.00] class: 0
  |   |   |   |   |   |   |   |--- ZIPCode_91129 >  0.50
  |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |   |--- ZIPCode_94705 >  0.50
  |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |--- ZIPCode_95822 >  0.50
  |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |--- ZIPCode_90049 >  0.50
  |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |--- ZIPCode_93106 >  0.50
  |   |   |   |   |--- weights: [1.00, 2.00] class: 1
  |   |   |--- CCAvg_Inc% >  37.50
  |   |   |   |--- Education <= 1.50
  |   |   |   |   |--- CD_Account <= 0.50
  |   |   |   |   |   |--- Experience <= 5.00
  |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |   |--- Experience >  5.00
  |   |   |   |   |   |   |--- ZIPCode_92709 <= 0.50
  |   |   |   |   |   |   |   |--- weights: [25.00, 1.00] class: 0
  |   |   |   |   |   |   |--- ZIPCode_92709 >  0.50
  |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
  |   |   |   |   |--- CD_Account >  0.50
  |   |   |   |   |   |--- weights: [1.00, 4.00] class: 1
  |   |   |   |--- Education >  1.50
  |   |   |   |   |--- Income <= 106.00
  |   |   |   |   |   |--- Family <= 2.50
  |   |   |   |   |   |   |--- CCAvg_Inc% <= 51.50
  |   |   |   |   |   |   |   |--- CD_Account <= 0.50
  |   |   |   |   |   |   |   |   |--- weights: [1.00, 5.00] class: 1
  |   |   |   |   |   |   |   |--- CD_Account >  0.50
  |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
  |   |   |   |   |   |   |--- CCAvg_Inc% >  51.50
  |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
  |   |   |   |   |   |--- Family >  2.50
  |   |   |   |   |   |   |--- Age <= 62.50
  |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1
  |   |   |   |   |   |   |--- Age >  62.50
  |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
  |   |   |   |   |--- Income >  106.00
  |   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
  |--- Income >  116.50
  |   |--- Education <= 1.50
  |   |   |--- Family <= 2.50
  |   |   |   |--- weights: [375.00, 0.00] class: 0
  |   |   |--- Family >  2.50
  |   |   |   |--- weights: [0.00, 47.00] class: 1
  |   |--- Education >  1.50
  |   |   |--- weights: [0.00, 222.00] class: 1

  

# importance of features in the tree building ( The importance of a feature is computed as the
  #(normalized) total reduction of the 'criterion' brought by that feature. It is also known as the Gini importance )

  print (pd.DataFrame(best_model.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))
  

                Imp
  Education     0.422
  Income        0.322
  Family        0.152
  CCAvg_Inc%    0.042
  CD_Account    0.018
  ...             ...
  ZIPCode_92096 0.000
  ZIPCode_92093 0.000
  ZIPCode_92084 0.000
  ZIPCode_92069 0.000
  ZIPCode_96651 0.000

  [478 rows x 1 columns]
  

comparison_frame = pd.DataFrame({'Model':['Initial decision tree model','Decision tree with restricted maximum depth','Decision treee with hyperparameter tuning',
                                           'Decision tree with post-pruning'], 'Train_Recall':[1,0.81,0.89,0.95], 'Test_Recall':[0.83,0.75,0.81,0.84]})
  comparison_frame
  

	Model	Train_Recall	Test_Recall
0	Initial decision tree model	1.000	0.830
1	Decision tree with restricted maximum depth	0.810	0.750
2	Decision treee with hyperparameter tuning	0.890	0.810
3	Decision tree with post-pruning	0.950	0.840

Decision Tree with post-pruning gives the highest recall on test set (0.84).

Comparing Logistic Regression and Decision Tree

The metric we are using to evaluate the Logistic Regression Model is F1 while we are using Recall to evauate the Decision Tree.

Decision Tree is easier to interpret than Logistic Regression, it also bisects the space into smaller spaces, it automatically handles decision making thresholds.

However, Decision tree is prone to overfitting and noise.

On the other hand, Logistic Regression is sturdy in the sense that is not prone to overifting and robust to noise. However is more complex, thresholds mut be set manually, and is more difficult to intrerpret.

if using Logistic Regression, consider using the Logistic Regression-0.37 Threshold that yields a Test F1 of 0.68.

If using Decision tree, consider using the Decision Tree with Post-Pruning that gives a Test Recall of 0.84.

Which model is better in our case or what model should we use? You can read this in our conclusions.

Conclusions

This Logistic Regression model with a threshold of 0.37 can predict with an accuracy of 0.961 on the training set and 0.945 on the testing set, which liability customers will borrow money (Personal Loan) while maintaining other bank Products. Our model has f1 score of 0.672 on the testing and 0.772 on the training sets.

The Decision Tree model w/ Post Pruning yields a recall of train 0.950 and of test 0.840 which are good.

In this particular case we would recommend trying the Decision tree model because not only is it easier to understand, but it can be visualized and its 'automatized' in the sense that since no threshold has to be chosen manually, this type of bias doesn't exist. We tried to minimize the overfitting and improve the model prunning it.

From the analysis, we determined that Income, CD Accounts, and Education are the most important features that influence whether a liability customer will purchase a Personal Loan Product.

• Higher income customers, tend to purchase loans more than lower income customers. Customers in the Income range of 120K to 190K have the not only the most Personal Loans compared to other income ranges but also the highest density of Personal Loans within their group.

• Customers with CD Accounts also tend to purchase more Personal Loans.

• Customers with higher education (Professional/Advanced) tend to purchase more Personal Loans.

Regarding our features that are less important than the 3 above, we have a few observations:

• The Age group in the 60s, 20s, and 30s, tend to purchase more loans than the other age groups.
• Family size 4 customers tend to purchase more loans.
• CC Average, customers that spend an average of 40-50% of their monthly income on a credit card, also tend to purchase more loans.
• Mortgage customers with monthly values of 5-15% of their monthly income also tend to purchase more loans.
• Security Accounts customers also tend to purchase more loans than those that do not have one.
• ZIP Code is a controversial observation. We noticed that ZIP codes in which a large university is located tend to purchase more loans. These customers are not necessarily students, but they may be associated to the schools in those ZIP code areas. In particular ZIP Codes 94720, 94305, 95616, 90095, and 93106 stand out.

Other features that don't seem to be relevant are Experience, Credit Card, and Online access.

Regarding our model, we use Logistic Regression-0.58 Threshold which yields the highest F1 score and if we use Decision Tree with post pruning, a recall of 0.95 (Train) and 0.84 (Test).

Recommendations

We dare to make the following recommendations:

Target the following Liability Customers:

1. Located in ZIP Codes where large or prestigious universities or colleges, including Silicon Valley.
2. Income over 100K.
3. With Higher Education (Advanced Degrees/Professional Degrees) first, then with Postgraduate education.

Fine Tune the Customers in the Target Group above (Preferably):

1. Have a CD_Account.
2. Have a Mortgage monthly payment is no more than 15% of their monthly income.
3. Do not have more than 50% of their monthly income on their monthly average credit card expense.
4. Have 4 or 3 family members.

If looking for new customers, we recommend attracting new customers in the ZIP Codes mentioned above and not in the undegraduate age group but in the age ranges of 30s to 60s.

Thank you.